# templates

name: Amazon CloudWatch Application Insights API with 27 Operations
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-01T04:08:39.781Z
updated_at: 2025-07-01T04:08:39.781Z
url: https://n8n.io/workflows/5498
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:21
description:
  Complete MCP server exposing 27 Amazon CloudWatch Application Insights API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community

  Import this workflow into your n8n instance
  Credentials Add Amazon CloudWatch Application Insights credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the Amazon CloudWatch Application Insights API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to http://applicationinsights.{region}.amazonaws.com
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (27 total)

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Createapplication (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.CreateApplication: Adds an application that is created from a resource group.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Createcomponent (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.CreateComponent: Creates a custom component by grouping similar standalone instances to monitor.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Createlogpattern (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.CreateLogPattern: Adds an log pattern to a LogPatternSet.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Deleteapplication (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.DeleteApplication: Removes the specified application from monitoring. Does not delete the application.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Deletecomponent (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.DeleteComponent: Ungroups a custom component. When you ungroup custom components, all applicable monitors that are set up for the component are removed and the instances revert to their standalone status.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Deletelogpattern (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.DeleteLogPattern: Removes the specified log pattern from a LogPatternSet.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Describeapplication (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.DescribeApplication: Describes the application.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Describecomponent (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.DescribeComponent: Describes a component and lists the resources that are grouped together in a component.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Describecomponentconfiguration (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.DescribeComponentConfiguration: Describes the monitoring configuration of the component.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Describecomponentconfigurationrecommendation (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.DescribeComponentConfigurationRecommendation: Describes the recommended monitoring configuration of the component.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Describelogpattern (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.DescribeLogPattern: Describe a specific log pattern from a LogPatternSet.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Describeobservation (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.DescribeObservation: Describes an anomaly or error with the application.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Describeproblem (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.DescribeProblem: Describes an application problem.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Describeproblemobservations (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.DescribeProblemObservations: Describes the anomalies or errors associated with the problem.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Listapplications (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.ListApplications: Lists the IDs of the applications that you are monitoring. 

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Listcomponents (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.ListComponents: Lists the auto-grouped, standalone, and custom components of the application.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Listconfigurationhistory (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.ListConfigurationHistory:  Lists the INFO, WARN, and ERROR events for periodic configuration updates performed by Application Insights. Examples of events represented are:    INFO: creating a new alarm or updating an alarm threshold.   WARN: alarm not created due to insufficient data points used to predict thresholds.   ERROR: alarm not created due to permission errors or exceeding quotas.   

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Listlogpatternsets (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.ListLogPatternSets: Lists the log pattern sets in the specific application.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Listlogpatterns (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.ListLogPatterns: Lists the log patterns in the specific log LogPatternSet.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Listproblems (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.ListProblems: Lists the problems with your application.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Listtagsforresource (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.ListTagsForResource: Retrieve a list of the tags (keys and values) that are associated with a specified application. A tag is a label that you optionally define and associate with an application. Each tag consists of a required tag key and an optional associated tag value. A tag key is a general label that acts as a category for more specific tag values. A tag value acts as a descriptor within a tag key.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Tagresource (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.TagResource: Add one or more tags (keys and values) to a specified application. A tag is a label that you optionally define and associate with an application. Tags can help you categorize and manage application in different ways, such as by purpose, owner, environment, or other criteria.  Each tag consists of a required tag key and an associated tag value, both of which you define. A tag key is a general label that acts as a category for more specific tag values. A tag value acts as a descriptor within a tag key.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Untagresource (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.UntagResource: Remove one or more tags (keys and values) from a specified application.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Updateapplication (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.UpdateApplication: Updates the application.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Updatecomponent (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.UpdateComponent: Updates the custom component name and/or the list of resources that make up the component.

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Updatecomponentconfiguration (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.UpdateComponentConfiguration: Updates the monitoring configurations for the component. The configuration input parameter is an escaped JSON of the configuration and should match the schema of what is returned by DescribeComponentConfigurationRecommendation. 

  🔧 #X-Amz-Target=Ec2Windowsbarleyservice.Updatelogpattern (1 endpoints)

  • POST /#X-Amz-Target=EC2WindowsBarleyService.UpdateLogPattern: Adds a log pattern to a LogPatternSet.

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native Amazon CloudWatch Application Insights API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
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workflow_json_compressed:
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metadata_json:
  {"categories":["DevOps","Monitoring","Cloud Infrastructure"],"complexity":"medium","use_cases":["Automated deployment monitoring","Real-time application health checks","Metric-driven alerting","AI agent integration"],"estimated_setup_minutes":10,"required_services":["Amazon CloudWatch","Amazon Web Services (AWS)","CloudWatch Application Insights"],"key_features":["27 API operation coverage","Direct MCP server implementation","AI agent accessibility","Comprehensive API exposure"],"target_audience":["DevOps engineers","Site reliability engineers (SREs)","Cloud architects"]}


name: Expose AWS Budgets API Operations to AI Agents via MCP Server
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-01T04:29:18.993Z
updated_at: 2025-07-01T04:29:18.993Z
url: https://n8n.io/workflows/5499
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator?

  Join the community

  Complete MCP server exposing 23 AWS Budgets API operations to AI agents.

  ⚡ Quick Setup

  Import this workflow into your n8n instance
  Credentials Add AWS Budgets credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the AWS Budgets API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://budgets.amazonaws.com
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (23 total)

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Createbudget (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.CreateBudget: Creates a budget and, if included, notifications and subscribers.  &lt;important&gt; Only one of BudgetLimit or PlannedBudgetLimits can be present in the syntax at one time. Use the syntax that matches your case. The Request Syntax section shows the BudgetLimit syntax. For PlannedBudgetLimits, see the Examples section.  &lt;/important&gt;

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Createbudgetaction (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.CreateBudgetAction:  Creates a budget action. 

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Createnotification (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.CreateNotification: Creates a notification. You must create the budget before you create the associated notification.

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Createsubscriber (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.CreateSubscriber: Creates a subscriber. You must create the associated budget and notification before you create the subscriber.

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Deletebudget (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.DeleteBudget: Deletes a budget. You can delete your budget at any time. &lt;important&gt; Deleting a budget also deletes the notifications and subscribers that are associated with that budget. &lt;/important&gt;

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Deletebudgetaction (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.DeleteBudgetAction:  Deletes a budget action. 

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Deletenotification (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.DeleteNotification: Deletes a notification. &lt;important&gt; Deleting a notification also deletes the subscribers that are associated with the notification. &lt;/important&gt;

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Deletesubscriber (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.DeleteSubscriber: Deletes a subscriber. &lt;important&gt; Deleting the last subscriber to a notification also deletes the notification. &lt;/important&gt;

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Describebudget (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.DescribeBudget: Describes a budget. &lt;important&gt; The Request Syntax section shows the BudgetLimit syntax. For PlannedBudgetLimits, see the Examples section.  &lt;/important&gt;

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Describebudgetaction (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.DescribeBudgetAction:  Describes a budget action detail. 

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Describebudgetactionhistories (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.DescribeBudgetActionHistories:  Describes a budget action history detail. 

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Describebudgetactionsforaccount (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.DescribeBudgetActionsForAccount:  Describes all of the budget actions for an account. 

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Describebudgetactionsforbudget (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.DescribeBudgetActionsForBudget:  Describes all of the budget actions for a budget. 

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Describebudgetnotificationsforaccount (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.DescribeBudgetNotificationsForAccount:  Lists the budget names and notifications that are associated with an account. 

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Describebudgetperformancehistory (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.DescribeBudgetPerformanceHistory: Describes the history for DAILY, MONTHLY, and QUARTERLY budgets. Budget history isn't available for ANNUAL budgets.

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Describebudgets (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.DescribeBudgets: Lists the budgets that are associated with an account. &lt;important&gt; The Request Syntax section shows the BudgetLimit syntax. For PlannedBudgetLimits, see the Examples section.  &lt;/important&gt;

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Describenotificationsforbudget (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.DescribeNotificationsForBudget: Lists the notifications that are associated with a budget.

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Describesubscribersfornotification (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.DescribeSubscribersForNotification: Lists the subscribers that are associated with a notification.

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Executebudgetaction (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.ExecuteBudgetAction:  Executes a budget action. 

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Updatebudget (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.UpdateBudget: Updates a budget. You can change every part of a budget except for the budgetName and the calculatedSpend. When you modify a budget, the calculatedSpend drops to zero until Amazon Web Services has new usage data to use for forecasting. &lt;important&gt; Only one of BudgetLimit or PlannedBudgetLimits can be present in the syntax at one time. Use the syntax that matches your case. The Request Syntax section shows the BudgetLimit syntax. For PlannedBudgetLimits, see the Examples section.  &lt;/important&gt;

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Updatebudgetaction (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.UpdateBudgetAction:  Updates a budget action. 

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Updatenotification (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.UpdateNotification: Updates a notification.

  🔧 #X-Amz-Target=Awsbudgetservicegateway.Updatesubscriber (1 endpoints)

  • POST /#X-Amz-Target=AWSBudgetServiceGateway.UpdateSubscriber: Updates a subscriber.

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native AWS Budgets API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

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workflow_json_compressed:
  [encoded data, 7652 characters]

metadata_json:
  {"categories":["DevOps","AI/ML"],"complexity":"medium","use_cases":["manage AWS budgets programmatically","integrate cloud cost monitoring with LLMs","enable AI agents to query budget data"],"estimated_setup_minutes":15,"required_services":["AWS Budgets"],"key_features":["MCP server integration","23 AWS Budgets API operations exposed","webhook-based triggering","JSON payload transformation"],"target_audience":["cloud engineers","devops engineers","AI developers"]}


name: AWS Cost & Usage Report Management for AI Agents
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-01T05:03:43.730Z
updated_at: 2025-07-01T05:03:43.730Z
url: https://n8n.io/workflows/5502
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:22
description:
  Complete MCP server exposing 4 AWS Cost and Usage Report Service API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community

  Import this workflow into your n8n instance
  Credentials Add AWS Cost and Usage Report Service credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the AWS Cost and Usage Report Service API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to http://cur.{region}.amazonaws.com
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (4 total)

  🔧 #X-Amz-Target=Awsorigamiservicegatewayservice.Deletereportdefinition (1 endpoints)

  • POST /#X-Amz-Target=AWSOrigamiServiceGatewayService.DeleteReportDefinition: Deletes the specified report.

  🔧 #X-Amz-Target=Awsorigamiservicegatewayservice.Describereportdefinitions (1 endpoints)

  • POST /#X-Amz-Target=AWSOrigamiServiceGatewayService.DescribeReportDefinitions: Lists the AWS Cost and Usage reports available to this account.

  🔧 #X-Amz-Target=Awsorigamiservicegatewayservice.Modifyreportdefinition (1 endpoints)

  • POST /#X-Amz-Target=AWSOrigamiServiceGatewayService.ModifyReportDefinition: Allows you to programatically update your report preferences.

  🔧 #X-Amz-Target=Awsorigamiservicegatewayservice.Putreportdefinition (1 endpoints)

  • POST /#X-Amz-Target=AWSOrigamiServiceGatewayService.PutReportDefinition: Creates a new report using the description that you provide.

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native AWS Cost and Usage Report Service API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["DevOps","AI/ML"],"complexity":"medium","use_cases":["Monitor AWS costs","Query usage reports","Manage AI agent budget","Audit cloud spending","Enforce cost limits"],"estimated_setup_minutes":5,"required_services":["AWS Cost and Usage Report Service","MCP Server"],"key_features":["API exposure","Cost monitoring","Usage tracking","Budget control"],"target_audience":["cloud architects","AI engineers","finops managers"]}


name: Generate Blogs with GPT-4o Prompt Chaining: Outline, Evaluate & Publish to Sheets
author_name: Rahul Joshi
author_username: rahul08
categories: []
created_at: 2025-07-01T06:25:18.849Z
updated_at: 2025-07-01T06:25:18.849Z
url: https://n8n.io/workflows/5504
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:21
description:
  Description

  This n8n automation template delivers a full-stack AI content pipeline designed for marketing teams, content creators, SaaS founders, and growth hackers. It combines prompt chaining, GPT-4o agents, and Google Sheets to generate engaging, SEO-friendly blogs—end to end.

  What This Template Does:

  📝 Generates blog topic ideas using a domain-specific AI agent (e.g., for Sparrow API testing)
  📄 Creates a blog outline with key sections and headings
  ✅ Evaluates & refines the outline to ensure clarity, flow, and engagement
  🧾 Writes the full blog content in structured, long-form paragraphs
  📥 Appends the blog to Google Sheets with the current date

  Built With:
  GPT-4o (via Azure OpenAI)
  LangChain Agents for task-specialized prompt chaining
  Google Sheets integration for automatic publishing
  Schedule Trigger for periodic content generation

  Ideal Use Cases:

  SaaS teams looking to scale inbound content
  API platforms (like Sparrow) publishing technical how-tos
  SEO agencies automating client blog content
  Solo founders growing product visibility via thought leadership

nodes_used:
  ["@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatAzureOpenAi","@n8n/n8n-nodes-langchain.lmChatAzureOpenAi","@n8n/n8n-nodes-langchain.lmChatAzureOpenAi","n8n-nodes-base.scheduleTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatAzureOpenAi","n8n-nodes-base.googleSheets"]

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metadata_json:
  {"categories":["AI/ML","Marketing","Content Creation"],"complexity":"medium","use_cases":["generate blog topics","create blog outlines","evaluate blog content","publish to spreadsheets"],"estimated_setup_minutes":15,"required_services":["GPT-4o","Google Sheets"],"key_features":["prompt chaining","AI agent orchestration","content evaluation","SEO optimization","full-stack automation"],"target_audience":["content creators","marketing teams","growth hackers"]}


name: Capture Website Form Submissions to Notion CRM Database
author_name: David Olusola
author_username: dae221
categories: []
created_at: 2025-07-01T06:44:09.863Z
updated_at: 2025-07-01T06:44:09.863Z
url: https://n8n.io/workflows/5505
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:21
description:
  ⚡️ How It Works

  This workflow captures form submissions from your website, formats the data, and automatically creates a new entry in your Notion CRM database. It eliminates manual copy-pasting and keeps your leads or requests organised in one place.

  🛠 Setup Steps
  	Webhook Node
  	•	Create a webhook in n8n.
  	•	Connect your website form to POST submissions to this webhook URL.
  	Code Node
  	•	Formats the incoming data to match your Notion database structure.
  	•	You can customise the fields in the code to suit your specific form inputs.
  	Notion Node (Create Page)
  	•	Connect your Notion account.
  	•	Choose your target database.
  	•	Map each field from the Code node output to your Notion database properties.
  	Test
  	•	Submit a test form entry.
  	•	Confirm the data appears correctly in Notion.

  ⸻

  👥 Who It’s For

  ✅ Freelancers collecting project inquiries
  ✅ Agencies managing client onboarding forms
  ✅ Business owners wanting organised lead capture
  ✅ Teams that use Notion as their central CRM or task manager
  ✅ Anyone tired of manually transferring form data into Notion

nodes_used:
  ["n8n-nodes-base.webhook","n8n-nodes-base.code","n8n-nodes-base.notion","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["Data Processing"],"complexity":"medium","use_cases":["capture website leads","automate CRM entries","format incoming form data"],"estimated_setup_minutes":15,"required_services":["n8n","Notion"],"key_features":["form submission capture","custom data validation","automated CRM storage"],"target_audience":["n8n users","CRM managers"]}


name: Auto-Stop Email Sequences Based on Replies with IMAP and Google Sheets
author_name: David Olusola
author_username: dae221
categories: []
created_at: 2025-07-01T07:08:46.874Z
updated_at: 2025-07-01T07:08:46.874Z
url: https://n8n.io/workflows/5506
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:21
description:
  Email Reply Auto-Stopper Workflow

  How It Works
  This n8n automation automatically stops your email sequences when prospects reply, preventing embarrassing follow-ups and maintaining professional outreach.

  The Process:

  Email Monitoring - IMAP trigger watches your inbox for new emails
  Reply Detection - When someone replies, it extracts their email address

  Database Lookup - Searches your Google Sheet prospect list for a match
  Auto-Stop - Updates the prospect's status to "STOP" and logs reply date
  Sequence Prevention - Your email sequences skip contacts with "STOP" status
  Real-World Example:

  You send Email 1 to John@clinic.com
  John replies: "Thanks, but not interested"
  This workflow automatically marks John as "STOP"
  Your follow-up sequences skip John completely
  You look professional, not spammy
  Setup Instructions
  Prerequisites
  n8n instance (cloud or self-hosted)
  Email account with IMAP access (Gmail, Outlook, etc.)
  Google Sheets with your prospect data
  Step 1: Prepare Your Google Sheet
  Your prospect sheet MUST include these columns:

  Email - Prospect email addresses
  Email Sent Status - Current status (ACTIVE, SENT, STOP, etc.)
  Reply Date - When they replied (can be empty initially)
  row_number - Unique identifier for each row
  Sample Sheet Structure:

  | First Name | Email | Email Sent Status | Reply Date | row_number |
  |------------|-------|-------------------|------------|------------|
  | John | john@clinic.com | ACTIVE | | 2 |
  | Sarah | sarah@therapy.com | SENT | | 3 |
  Step 2: Set Up Email Credentials
  In n8n, go to Credentials → Add Credential
  Choose IMAP
  Enter your email settings:
  Host: imap.gmail.com (for Gmail)
  Port: 993
  Username: your-email@gmail.com
  Password: your-app-password
  Secure: Yes
  Gmail Users: Enable 2FA and create an App Password

  Step 3: Set Up Google Sheets Access
  Create Google Sheets OAuth2 credential
  Follow n8n's Google Sheets setup guide
  Grant access to your prospect spreadsheet
  Step 4: Import & Configure Workflow
  Import the workflow JSON
  Email Trigger Node:
  Select your IMAP credential
  Keep "UNSEEN" filter enabled
  Google Sheets Nodes:
  Select your Google Sheets credential
  Choose your prospect spreadsheet
  Select correct sheet tab
  Test the workflow with a test email
  Step 5: Connect to Your Email Sequences
  Your existing email automation should check the "Email Sent Status" column:

  Skip sending if status = "STOP"
  Continue sequence if status = "ACTIVE" or "SENT"
  Important Notes
  ⚠️ Critical: Your email sequence automation MUST respect the "Email Sent Status" field, or this won't work.

  💡 Pro Tip: Test with a personal email first to make sure the matching works correctly.

  🔧 Customization: You can modify the "STOP" status to anything that fits your workflow (REPLIED, UNSUBSCRIBED, etc.).

  Use Cases
  Cold email campaigns
  Sales follow-up sequences
  Newsletter nurture flows
  Event promotion follow-ups
  Customer onboarding sequences
  Troubleshooting
  Emails not being detected:

  Check IMAP credentials
  Verify "UNSEEN" filter is working
  Test with manual email trigger
  Sheet not updating:

  Confirm Google Sheets permissions
  Check column names match exactly
  Verify row_number column exists
  No matches found:

  Email addresses must match exactly (case-insensitive)
  Check for extra spaces in email data
  Verify prospect is in the correct sheet tab
  This automation prevents spam complaints, protects sender reputation, and keeps your outreach professional. Set it once, forget it forever.

nodes_used:
  ["n8n-nodes-base.emailReadImap","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets","n8n-nodes-base.code","n8n-nodes-base.googleSheets"]

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metadata_json:
  {"categories":["Communication","Marketing"],"complexity":"complex","use_cases":["Automated email sequence management","Prospect engagement tracking","Smart outreach stopping"],"estimated_setup_minutes":15,"required_services":["Google Sheets","IMAP Email Provider"],"key_features":["Real-time inbox monitoring","Automated prospect list matching","Dynamic workflow state updates"],"target_audience":["Outreach managers","Sales professionals","Marketing managers"]}


name: Analyze Images from Forms using GPT-4o-mini Vision and Deliver to Telegram
author_name: David Olusola
author_username: dae221
categories: []
created_at: 2025-07-01T07:27:52.589Z
updated_at: 2025-07-01T07:27:52.589Z
url: https://n8n.io/workflows/5507
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:20
description:
  This workflow analyzes images submitted via a form using OpenAI Vision, then delivers the analysis result directly to your Telegram chat.

  ✅ Use case examples:
  	•	Users submit screenshots for instant AI interpretation
  	•	Automated document or receipt analysis with Telegram delivery
  	•	Quick OCR or image classification workflows

  ⸻

  ⚙️ Setup Guide
  	Form Submission Trigger
  	•	Connect your form app (e.g. Typeform, Tally, or n8n’s own webhook form) to the On form submission trigger node.
  	•	Ensure it sends the image file or URL as input.
  	OpenAI Vision Analysis
  	•	In the OpenAI node, select Analyze Image operation.
  	•	Provide your OpenAI API key and configure the prompt to instruct the model on what to analyze (e.g. “Describe this receipt in detail”).
  	Set Telegram Chat ID
  	•	Use this manual node to input your Telegram Chat ID for delivery.
  	•	Alternatively, automate this with a database lookup or user session if building for multiple users.
  	Telegram Delivery Node
  	•	Connect your Telegram Bot to n8n using your bot token.
  	•	Set up the sendMessage operation, using the analysis result from the previous node as the message text.
  	Testing
  	•	Click Execute workflow.
  	•	Submit an image via your form and confirm it delivers to your Telegram as expected.

nodes_used:
  ["n8n-nodes-base.formTrigger","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.set","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["AI/ML","Communication","Data Processing"],"complexity":"simple","use_cases":["analyze images from forms","deliver AI insight to Telegram","automated receipt review"],"estimated_setup_minutes":5,"required_services":["OpenAI","Telegram"],"key_features":["Visual content interpretation","Cross-platform notification","Image-to-text conversion"],"target_audience":["developers","marketers"]}


name: Create a Private Document Q&A System with Llama3, Postgres, Qdrant and Google Drive
author_name: David Olusola
author_username: dae221
categories: []
created_at: 2025-07-01T07:42:55.735Z
updated_at: 2025-07-01T07:42:55.735Z
url: https://n8n.io/workflows/5508
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:20
description:
  ⚙️ How It Works: LocalRAG.AI

  ⚠️ Note: This system only works for self-hosted n8n instances. It will not function on n8n.cloud or other remote setups.
  LocalRAG.AI is a private, on-prem AI assistant that uses your own documents to answer questions intelligently. It combines LangChain, Ollama, Qdrant, and Postgres into a powerful AI pipeline — all running locally for maximum data privacy.

  🔄 What It Does
  Monitors Your Google Drive Folders for new or updated files.
  Downloads the file, extracts the text, and prepares it.
  Generates Embeddings using your local Ollama model (e.g., LLaMA 3).
  Stores them in Qdrant, your local vector database.
  During a chat, it:
  Uses vector search to retrieve relevant chunks.
  Combines them with chat history stored in Postgres.
  Responds via a LangChain AI agent using your local model.
  🛠️ Setup Steps (Self-hosted Only)
  Install and Self-host n8n (e.g., via Docker).
  Set up your Ollama instance locally and load your desired LLM (e.g., llama3).
  Deploy Qdrant locally for vector storage.
  Connect a Postgres DB to store chat history.
  Create and import the workflow in n8n.
  Authenticate Google Drive to monitor folders.
  Connect credentials for Ollama, Qdrant, Postgres in the n8n workflow.
  Start chatting through the Webhook Trigger or custom UI.
  🧠 Perfect For:
  Research teams handling confidential data
  Internal documentation Q&A
  AI chatbots that don’t rely on OpenAI or cloud

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOllama","@n8n/n8n-nodes-langchain.memoryPostgresChat","@n8n/n8n-nodes-langchain.vectorStoreQdrant","@n8n/n8n-nodes-langchain.lmOllama","@n8n/n8n-nodes-langchain.embeddingsOllama","@n8n/n8n-nodes-langchain.toolVectorStore","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.set","n8n-nodes-base.googleDrive","n8n-nodes-base.extractFromFile","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter","@n8n/n8n-nodes-langchain.embeddingsOllama","@n8n/n8n-nodes-langchain.vectorStoreQdrant","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["AI/ML","Data Processing","Security"],"complexity":"medium","use_cases":["ask questions based on company documents","perform secure internal knowledge retrieval","build private RAG applications","monitor shared drives for updates","extract context from internal resources"],"estimated_setup_minutes":120,"required_services":["Ollama","Postgres","Qdrant","Google Drive"],"key_features":["local data processing","private AI responses","embedded knowledge base","Google Drive integration","RAG pipeline implementation"],"target_audience":["data scientists","security engineers","knowledge managers"]}


name: AI-Powered Contact Management in Airtable with Natural Language Commands
author_name: David Olusola
author_username: dae221
categories: []
created_at: 2025-07-01T08:01:44.545Z
updated_at: 2025-07-01T08:01:44.545Z
url: https://n8n.io/workflows/5509
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:21
description:
  AI-Powered Airtable Contact Manager

  Overview

  The AI-Powered Airtable Contact Manager is an intelligent n8n workflow that enables AI assistants to seamlessly manage contact data in Airtable through natural language interactions. Using the Model Context Protocol (MCP), this workflow bridges the gap between conversational AI and structured data management.

  How It Works

  This workflow creates a powerful AI-to-database interface that allows users to manage their Airtable contacts through natural language commands. Here's the complete flow:

  1. AI Interaction Layer
  Users interact with an AI assistant using natural language
  Examples: "Add John Doe to contacts", "Find all contacts assigned to Sarah", "Show me contact details for ID xyz"

  2. MCP Server Trigger
  The AI assistant processes the user's request and identifies the needed operation
  Sends structured commands to the n8n workflow via the MCP (Model Context Protocol)
  Acts as the intelligent routing system for all contact operations

  3. Airtable Operations
  The workflow provides four core contact management functions:

  🔍 Get Record: Retrieves specific contact details using a Record ID
  Input: Record ID from AI
  Output: Complete contact information (Name, Email, Assignee, Status)

  ➕ Create Record: Adds new contacts to the database
  Input: Contact details (Name, Email, Assignee)
  Output: New record with auto-generated ID and default status

  🗑️ Delete Record: Removes contacts permanently
  Input: Record ID to delete
  Output: Confirmation of successful deletion

  🔎 Search Records: Finds contacts using flexible criteria
  Input: Airtable formula for filtering
  Output: All matching contact records

  4. Smart Data Handling
  The workflow uses AI-powered field mapping with $fromAI() functions
  Automatically extracts relevant information from natural language requests
  Maintains data integrity with proper field validation

  Setup Steps

  Prerequisites
  n8n instance (cloud or self-hosted)
  Airtable account with API access
  MCP-compatible AI system

  Step 1: Airtable Preparation
  Create Airtable Base:
     Set up a new base or use existing one
     Note your Base ID (starts with app)

  Set Up Contact Table:
     Create a table with these fields:
       Name (Single line text)
       email (Email)
       Assignee (Single line text)
       Status (Single select: Todo, In progress, Done)
     Note your Table ID (starts with tbl)

  Generate API Token:
     Go to https://airtable.com/developers/web/api/introduction
     Create a personal access token with full permissions
     Save this token securely

  Step 2: n8n Configuration
  Import Workflow:
     Copy the enhanced JSON workflow
     Import into your n8n instance

  Configure Airtable Credentials:
     Go to Credentials in n8n
     Create new "Airtable Personal Access Token" credential
     Enter your Airtable API token
     Name it "full access" (or update credential references in workflow)

  Update Base and Table IDs:
     Replace YOUR_AIRTABLE_BASE_ID with your actual Base ID (starts with app)
     Replace YOUR_AIRTABLE_TABLE_ID with your actual Table ID (starts with tbl)
     Update in all four Airtable nodes

  Update Credential References:
     Replace your-airtable-credential-id with your actual credential ID
     Or rename your credential to match "Airtable API Token"

  Step 3: MCP Integration
  Configure MCP Server:
     Set up your MCP server to communicate with n8n
     Replace your-webhook-path-here and your-webhook-id-here with your actual webhook details
     Configure your AI system to use this workflow

  Update Node IDs (Optional):
     The workflow uses placeholder node IDs for privacy
     n8n will auto-generate new IDs when you import
     No action needed unless you're referencing specific nodes

  Test the Integration:
     Activate the workflow in n8n
     Test each operation through your AI interface
     Verify data flows correctly between AI and Airtable

  Step 4: Customization (Optional)
  Add More Fields:
     Extend the Airtable schema as needed
     Update the Create Record node field mappings
     Modify the Search Record filters

  Enhanced Error Handling:
     Add error handling nodes
     Set up notifications for failed operations
     Implement retry logic for reliability

  Usage Examples

  Once set up, users can interact with the system naturally:

  Creating Contacts:
  "Add Sarah Johnson with email sarah@company.com, assign to Mike"
  "Create a new contact for David Wilson, email david@startup.io"

  Finding Contacts:
  "Show me all contacts assigned to Jennifer"
  "Find contacts with status 'In progress'"
  "Search for contacts with gmail addresses"

  Managing Records:
  "Get details for contact rec123ABC"
  "Delete the contact with ID rec456DEF"
  "Update John's status to Done"

  Benefits

  Natural Language Interface**: No technical knowledge required
  Automated Data Entry**: Reduces manual work and errors
  Flexible Searching**: Find contacts using any criteria
  AI-Powered**: Leverages advanced language understanding
  Scalable**: Easily extend with more operations
  Integrated**: Works seamlessly with existing Airtable workflows

  Technical Notes

  Uses n8n's $fromAI() function for intelligent data extraction
  Implements MCP for standardized AI-to-automation communication
  Supports Airtable's formula syntax for complex searches
  Maintains security through proper credential management
  Designed for high reliability with error handling capabilities

  This workflow transforms contact management from a manual, time-consuming task into an effortless, conversational experience powered by AI.

nodes_used:
  ["@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.airtableTool","n8n-nodes-base.airtableTool","n8n-nodes-base.airtableTool","n8n-nodes-base.airtableTool","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["AI/ML","Data Processing","Communication"],"complexity":"medium","use_cases":["manage contact data","process natural language","query databases"],"estimated_setup_minutes":15,"required_services":["Airtable","notebookLM","MCP"],"key_features":["AI-driven search","natural language processing","structured data export"],"target_audience":["database admins","customer support leads","data analysts"]}


name: Monitor Dynamic Website Changes with Firecrawl, Sheets & Gmail Alerts
author_name: Franz
author_username: agents-by-franz
categories: []
created_at: 2025-07-01T08:14:05.491Z
updated_at: 2025-07-01T08:14:05.491Z
url: https://n8n.io/workflows/5510
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:21
description:
  🕸️ Dynamic Website Change Monitor with Smart Email Alerts

  Never miss important website updates again! This workflow automatically tracks changes on dynamic websites (think React apps, JavaScript-heavy sites) and sends you instant email notifications when something changes. Perfect for keeping tabs on competitors, monitoring product updates, or staying on top of important announcements.

  ✨ What makes this special?

  🚀 Handles Dynamic Websites: Uses Firecrawl API to scrape JavaScript-rendered content that basic scrapers can't touch
  📧 Smart Email Alerts: Only sends notifications when content actually changes (no spam!)
  📊 Historical Tracking: Keeps a complete log of all changes in Google Sheets
  🛡️ Bulletproof: Continues working even if one part fails
  ⚡ Ready to Deploy: Webhook-triggered, perfect for cron jobs or external schedulers

  🎯 Perfect for monitoring:
  Competitor pricing pages
  Job board postings
  Product availability updates  
  News sites for breaking stories
  API documentation changes
  Terms of service updates

  🛠️ What you'll need to get started:

  API Accounts & Keys:
  Firecrawl Account 🔥
     Sign up at firecrawl.dev
     Grab your API key from the dashboard
     Create a "Bearer Auth" credential in n8n

  Google Cloud Setup ☁️
     Enable Google Sheets API
     Enable Gmail API  
     Set up OAuth2 credentials
     Add both as credentials in n8n

  Google Sheets Document 📋
     Create a new spreadsheet
     Add two tabs: "Log" and "comparison"
     Follow the structure outlined in the workflow notes

  🚀 How it works:

  Webhook receives trigger → Starts the monitoring process
  Firecrawl scrapes website → Gets fresh content (even JavaScript-rendered!)
  Smart comparison → Checks against previously stored content
  Change detected? → If yes, send email + log everything
  Update storage → Prepares for next monitoring cycle

  ⚙️ Setup Steps:

  Import this workflow into your n8n instance
  Configure credentials for Firecrawl, Google Sheets, and Gmail
  Update the target URL in the Firecrawl node
  Set your email address in the Gmail node
  Create your Google Sheets with the required structure
  Test it manually first, then activate!

  🎨 Customize it your way:

  Target any website** by updating the URL
  Change email templates** to match your style
  Adjust monitoring frequency** with external cron jobs
  Switch between markdown/HTML** extraction formats
  Fine-tune change detection** sensitivity

  🔧 Troubleshooting:

  Firecrawl errors?** Check your API key and rate limits
  Google Sheets issues?** Verify OAuth permissions and sheet structure  
  Email not sending?** Check Gmail API quotas and spam folders
  Webhook problems?** Make sure the workflow is activated

  Ready to never miss another website change? Let's get this automation running! 🎉

nodes_used:
  ["n8n-nodes-base.webhook","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.if","n8n-nodes-base.respondToWebhook","n8n-nodes-base.googleSheets","n8n-nodes-base.code","n8n-nodes-base.gmail","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.respondToWebhook","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 7524 characters]

metadata_json:
  {"categories":["Data Processing"],"complexity":"medium","use_cases":["monitor dynamic websites","detect content updates","automate change alerts"],"estimated_setup_minutes":5,"required_services":["Firecrawl API","Google Sheets","Gmail/SMTP API"],"key_features":["Scrapes JavaScript-rendered websites","Sends email notifications","Tracks dynamic content changes","Stores data in spreadsheets"],"target_audience":["marketers","product managers","content analysts"]}


name: Multilingual Voice & Text Telegram Bot with ElevenLabs TTS and LangChain Agents
author_name: IvanCore
author_username: ivancore
categories: []
created_at: 2025-07-01T09:21:27.881Z
updated_at: 2025-07-01T09:21:27.881Z
url: https://n8n.io/workflows/5511
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:21
description:
  This workflow contains community nodes that are only compatible with the self-hosted version of n8n. 

  Telegram Voice AI Assistant
  This n8n template creates a multimodal Telegram bot that dynamically responds to users:

  Replies with voice** when receiving voice messages (using ElevenLabs TTS)

  Replies with text** for text-based queries

  Supports custom AI tools (e.g., crypto APIs, databases, or custom functions)

  Built with LangChain Agents, it can integrate any external API or data source into conversations.

  Key Features
  🎙️ Smart Response Logic
  Voice Query? → Voice Reply**
  Transcribes audio via ElevenLabs STT
  Processes with AI (Groq/Gemini)
  Converts text response to natural speech (ElevenLabs TTS)

  Text Query? → Text Reply**
  Bypasses TTS/STT for faster responses

  🛠️ Extensible AI Tools
  Add your own tools:
  Database lookups
  Weather/stock APIs
  Custom Python functions
  RAG (document retrieval)

  Supports multi-step tool chaining (e.g., "Get BTC price → analyze trends → summarize")

  🌐 Language & Context
  Auto-detects user language (via Telegram’s language_code)
  Maintains session memory (remembers conversation history)

  Use Cases
  Voice-first customer support**
  Crypto/analytics assistants** (e.g., "What’s Ethereum’s current gas fee?")
  Multilingual FAQ bots**
  Educational tutors** (voice-interactive learning)

  Requirements
  Telegram Bot Token**
  ElevenLabs API Key** (For TTS/STT)
  Groq API Key* or *Google Gemini API Key**

  Customization Tips
  Change AI personality*: Modify the *systemMessage in the Voice Assistant node
  Add more models**: Swap Groq/Gemini for OpenAI, Anthropic, etc.
  Extend functionality**: Add RAG (Retrieval-Augmented Generation) for document queries

  Take this template to create a Siri-like AI assistant for Telegram in minutes! 🚀

nodes_used:
  ["n8n-nodes-base.telegramTrigger","n8n-nodes-base.set","n8n-nodes-base.switch","n8n-nodes-base.telegram","@elevenlabs/n8n-nodes-elevenlabs.elevenLabs","n8n-nodes-base.set","n8n-nodes-base.aggregate","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.lmChatGroq","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.if","@elevenlabs/n8n-nodes-elevenlabs.elevenLabs","n8n-nodes-base.telegram","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["Communication","AI/ML"],"complexity":"medium","use_cases":["respond to voice messages","generate text replies","integrate custom AI tools","enable multimodal interactions","deploy voice AI assistants"],"estimated_setup_minutes":30,"required_services":["ElevenLabs","Firebase Messaging","Telegram API"],"key_features":["voice to text processing","text to TTS conversion","LangChain agent integration","custom API support","multimodal response handling"],"target_audience":["developers","bot creators","customer support teams"]}


name: Send Daily Currency Exchange Rate Updates via CurrencyFreaks API and Gmail
author_name: Sarfaraz Muhammad Sajib
author_username: sarfarazmuhammad
categories: []
created_at: 2025-07-01T10:53:48.034Z
updated_at: 2025-07-01T10:53:48.034Z
url: https://n8n.io/workflows/5512
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:22
description:
  Daily Currency Update Workflow (n8n)

  Trigger: ScheduleTrigger node (configurable interval)
  Set Variables: API Key, Preferred Currencies (PKR, GBP, EUR, USD, BDT, INR)
  HTTP Request: Fetch latest exchange rates from CurrencyFreaks API
  Set Recipient Email
  Set Email Subject
  Send Email: HTML formatted via Gmail OAuth2 with dynamic rate data (date, base currency, rates)
  Flow: ScheduleTrigger → Set API Key & Currencies → HTTP Request → Set Recipient → Set Subject → Gmail Send

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.gmail","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["Finance","Communication"],"complexity":"simple","use_cases":["automated currency data delivery","daily exchange rate reporting","schedule-based email notifications"],"estimated_setup_minutes":15,"required_services":["CurrencyFreaks API","Gmail"],"key_features":["scheduled API data fetching","dynamic email content","multi-currency support"],"target_audience":["finance professionals","accountants","business owners"]}


name: Automate FAQ Responses with WhatsApp Keyword Detection Bot
author_name: Solido AI
author_username: solidoai
categories: []
created_at: 2025-07-01T14:23:34.677Z
updated_at: 2025-07-01T14:23:34.677Z
url: https://n8n.io/workflows/5516
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:21
description:
  How it works:
  This bot operates in a continuous WhatsApp monitoring loop. It analyzes messages to detect keywords in common questions (like hours, prices, and location) and sends automatic replies with predefined information. For unrecognized questions, it directs the user to manual assistance.

  Set up steps:
  The initial setup involves integrating with the WhatsApp API, registering keywords and their respective responses, and defining the fallback flow. It takes only a few minutes to have the bot running with essential information.

nodes_used:
  ["n8n-nodes-base.whatsAppTrigger","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.switch","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.whatsApp","n8n-nodes-base.whatsApp","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 5880 characters]

metadata_json:
  {"categories":["Communication","Customer Support"],"complexity":"medium","use_cases":["autorespond FAQ queries","detect keywords","route queries to human support"],"estimated_setup_minutes":25,"required_services":["WhatsApp API"],"key_features":["keyword pattern matching","predefined response triggering","fallback routing logic"],"target_audience":["customer support agents","business owners","community managers"]}


name: Manage Personal Expenses with Webhooks and Google Sheets Automated Tracker
author_name: Solido AI
author_username: solidoai
categories: []
created_at: 2025-07-01T14:24:38.880Z
updated_at: 2025-07-01T14:24:38.880Z
url: https://n8n.io/workflows/5517
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:21
description:
  How it works:
  This system functions by receiving expenses via webhook POST. It validates the data, stores it in Google Sheets, and, daily at 8 PM, generates and sends financial summaries. Automatic categorization simplifies the organization of expenses.

  Set up steps:
  Setup involves creating the Google Sheet, configuring the webhook, and defining the categorization rules. The process is quick and intuitive, taking about 10-15 minutes for the system to be ready to receive your expenses.

nodes_used:
  ["n8n-nodes-base.webhook","n8n-nodes-base.function","n8n-nodes-base.googleSheets","n8n-nodes-base.function","n8n-nodes-base.respondToWebhook","n8n-nodes-base.respondToWebhook","n8n-nodes-base.cron","n8n-nodes-base.googleSheets","n8n-nodes-base.function","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 4512 characters]

metadata_json:
  {"categories":["Personal Finance","Automation","Data Entry"],"complexity":"medium","use_cases":["Automate expense tracking","Summarize financial data","Categorize daily expenses"],"estimated_setup_minutes":10,"required_services":["Google Sheets","Webhooks"],"key_features":["Webhook input","Google Sheets storage","Daily summarization","Automatic categorization"],"target_audience":["Personal finance managers","Small business owners","Admin users"]}


name: Automate Gmail Inbox Organization with Rule-based Categorization and Smart Actions
author_name: Solido AI
author_username: solidoai
categories: []
created_at: 2025-07-01T14:25:20.168Z
updated_at: 2025-07-01T14:25:20.168Z
url: https://n8n.io/workflows/5518
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:21
description:
  How it works:
  The organizer continuously monitors your Gmail inbox. It analyzes sender and subject to categorize emails (Work, Purchases, Newsletter) and automatically applies labels. Based on the category, it performs specific actions, such as marking important emails or archiving newsletters.

  Set up steps:
  The initial setup requires Gmail access permission and defining the categorization rules and desired actions for each email type. This process can be configured in approximately 5 to 10 minutes, depending on the complexity of the rules you wish to establish.

nodes_used:
  ["n8n-nodes-base.gmailTrigger","n8n-nodes-base.switch","n8n-nodes-base.gmail","n8n-nodes-base.gmail","n8n-nodes-base.gmail","n8n-nodes-base.gmail","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["Communication","Productivity"],"complexity":"medium","use_cases":["Email categorization","Auto-labeling","Archive management"],"estimated_setup_minutes":10,"required_services":["Gmail"],"key_features":["Sender analysis","Subject parsing","Rule-based categorization","Automated labeling","Conditional actions"],"target_audience":["administrators","desk managers"]}


name: Process Documents with Recursive Chunking using Google Drive, OpenAI & Gemini RAG
author_name: Mohsin Ali
author_username: binbakhsh
categories: []
created_at: 2025-07-01T15:06:26.131Z
updated_at: 2025-07-01T15:06:26.131Z
url: https://n8n.io/workflows/5521
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:20
description:
  1. Document Ingestion & Processing

  Google Drive Trigger monitors for new files → Loop Over Items processes each file → File Info extracts metadata → Google Drive downloads the actual content → Switch routes to appropriate extractors (PDF or TEXT) based on file type

  2. Content Transformation & Chunking

  Document Data node processes extracted text → Recursive Splitter breaks content into contextual chunks → Chunk Splitting applies intelligent segmentation while preserving document context and relationships between chunks

  3. Embedding & Storage

  Basic LLM Chain processes chunks → OpenAI Chat Model generates contextual understanding → Summarize creates document summaries → Supabase Vector Store saves embeddings with metadata → Embeddings OpenAI creates vector representations → Default Data Loader handles storage operations

  4. Query Processing & Retrieval

  When Clicking Execute triggers user queries → OpenAI processes and understands the question → AI Agent orchestrates hybrid search (combining vector similarity + keyword matching) → Google Gemini Chat Model generates final responses using retrieved context → HTTP Request handles additional external data sources

nodes_used:
  ["n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.splitInBatches","n8n-nodes-base.googleDrive","n8n-nodes-base.switch","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.summarize","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.vectorStoreSupabase","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterCharacterTextSplitter","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.set","n8n-nodes-base.extractFromFile","n8n-nodes-base.extractFromFile","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.splitOut","n8n-nodes-base.httpRequestTool"]

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metadata_json:
  {"categories":["AI/ML","Data Processing"],"complexity":"medium","use_cases":["process documents","chunk content for RAG","ingest files from drive","extract and split text"],"estimated_setup_minutes":10,"required_services":["Google Drive","OpenAI","Gemini","Supabase"],"key_features":["recursive chunking","context-aware splitting","multi-format extraction","RAG preparation","batch processing"],"target_audience":["data engineers","AI developers","data analysts"]}


name: Convert and Manipulate PDFs with Api2Pdf and AWS Lambda
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-01T17:11:54.298Z
updated_at: 2025-07-01T17:11:54.298Z
url: https://n8n.io/workflows/5522
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Complete MCP server exposing 9 Api2Pdf - PDF Generation, Powered by AWS Lambda API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community
  Import this workflow into your n8n instance
  Credentials Add Api2Pdf - PDF Generation, Powered by AWS Lambda credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the Api2Pdf - PDF Generation, Powered by AWS Lambda API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://v2018.api2pdf.com
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (9 total)

  🔧 Chrome (3 endpoints)

  • POST /chrome/html: Convert raw HTML to PDF
  • GET /chrome/url: Convert URL to PDF
  • POST /chrome/url: Convert URL to PDF

  🔧 Libreoffice (1 endpoints)

  • POST /libreoffice/convert: Convert office document or image to PDF

  🔧 Merge (1 endpoints)

  • POST /merge: Merge multiple PDFs together

  🔧 Wkhtmltopdf (3 endpoints)

  • POST /wkhtmltopdf/html: Convert raw HTML to PDF
  • GET /wkhtmltopdf/url: Convert URL to PDF
  • POST /wkhtmltopdf/url: Convert URL to PDF

  🔧 Zebra (1 endpoints)

  • GET /zebra: Generate bar codes and QR codes with ZXING.

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native Api2Pdf - PDF Generation, Powered by AWS Lambda API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool"]

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metadata_json:
  {"categories":["AI/ML","DevOps","Data Processing"],"complexity":"medium","use_cases":["Generate PDFs programmatically","Convert documents remotely","Integrate PDF tools with AI agents","Build MCP server customizations"],"estimated_setup_minutes":45,"required_services":["Api2Pdf - PDF Generation, Powered by AWS Lambda","AWS Lambda"],"key_features":["MCP server exposure for AI agents","HTTP/Webhook trigger integration","PDF manipulation capabilities","Remote PDF generation"],"target_audience":["Developer","DevOps Engineer","Automation Specialist"]}


name: Evaluate tool usage accuracy in multi-agent AI workflows using Evaluation nodes
author_name: Angel Menendez
author_username: djangelic
categories: []
created_at: 2025-07-01T17:12:40.342Z
updated_at: 2025-07-01T17:12:40.342Z
url: https://n8n.io/workflows/5523
scraped_at: 2026-05-02 08:04:48
metadata_generated_at: 2026-05-02 08:36:19
description:
  Who's it for
  This workflow is ideal for AI developers running multi-agent systems in n8n who need to quantitatively evaluate tool usage behavior. If you're building autonomous agents and want to verify their decisions against ground-truth expectations, this workflow gives you plug-and-play observability.

  What it does
  This template uses n8n's built-in Evaluation Trigger and Evaluation nodes to assess whether an AI agent correctly used all the expected tools. It supports:

  Dataset-driven testing of agent behavior

  Logging actual tools to compare them with the expected tools

  Assigning performance metrics (tool_called = true/false)

  Persisting output back to Google Sheets for further debugging

  The workflow can be triggered by either the chat input or the dataset row evaluation. It routes through a multi-tool agent node powered by the best LLMs. The agent has access to tools such as web search, calculator, vector search, and summarizer tools. The workflow then aims to validate tool use decisions by extracting the intermediate steps from the agent (i.e., action + observation) and comparing the tools that were called with the expected tools. If the tools that were called during the workflow execution match, then it's a pass; otherwise, it's documented as a fail. The evaluation nodes take care of that process. 

  How to set it up
  Connect your Google Sheets OAuth2 credential. Replace the document with your own test dataset.

  Set your desired models and configure the different agent tools, such as the summarizer and vector store. The default vector store used is Qdrant, so the user must create this vector store with a few samples of queries + web search results.

  Run from either the chat trigger or the evaluation trigger to test.

  Requirements
  Google Sheets OAuth2 credential

  OpenRouter / OpenAI credentials for AI agents and embeddings

  Firecrawl and Qdrant credentials for web + vector search

  How to customize
  Edit the Search Agent system message to define tool selection behavior

  Add more metric columns in the Evaluation node for complex scoring

  Add new tool nodes and link them to the agent block

  Swap in your own summarizer

nodes_used:
  ["@n8n/n8n-nodes-langchain.toolCalculator","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.set","n8n-nodes-base.noOp","n8n-nodes-base.evaluationTrigger","n8n-nodes-base.evaluation","n8n-nodes-base.evaluation","n8n-nodes-base.evaluation","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.httpRequestTool","@n8n/n8n-nodes-langchain.lmChatOpenRouter","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.vectorStoreQdrant"]

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metadata_json:
  {"categories":["AI/ML"],"complexity":"medium","use_cases":["evaluate agent tool usage","verify autonomous agent decisions","test multi-agent workflows"],"estimated_setup_minutes":10,"required_services":["OpenRouter","Qdrant"],"key_features":["dataset-driven testing","quantitative evaluation","evaluated tool usage behavior","built-in evaluation nodes"],"target_audience":["AI developers","machine learning engineers"]}


name: Generate & Email Personalized Jokes with GPT-4o-mini and Gmail
author_name: Oriol Seguí
author_username: oxsr11
categories: []
created_at: 2025-07-01T17:55:38.306Z
updated_at: 2025-07-01T17:55:38.306Z
url: https://n8n.io/workflows/5525
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:21
description:
  This fun workflow automates the generation and delivery of personalized jokes by email based on the names or objects entered in the form. The process works as follows:

  On form submission
  The workflow starts when someone submits a form with the required names or objects to create the joke.
  You can modify the form fields to make the jokes more creative.

  Set the output language
  Manually define the language in which you want to receive the joke.

  OpenAI Message Model
  Uses the OpenAI model to generate the joke based on the prompt and in the chosen language.
  (The response is limited to around 200 tokens.)

  Gmail: send message
  The generated joke is automatically sent to the specified email address via Gmail.

nodes_used:
  ["n8n-nodes-base.formTrigger","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["AI/ML","Communication"],"complexity":"simple","use_cases":["Generate personalized jokes","Send email jokes","Form-based input processing","Language selection"],"estimated_setup_minutes":10,"required_services":["OpenAI","Google Forms","Gmail"],"key_features":["Personalized content generation","Email delivery automation","Multi-language support via prompts","Form-to-action workflow trigger"],"target_audience":["Marketing teams","Customer support","Fun team building activities"]}


name: Create a WHOIS API Interface for AI Agents with 8 Domain Management Operations
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-01T19:19:26.667Z
updated_at: 2025-07-01T19:19:26.667Z
url: https://n8n.io/workflows/5528
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:21
description:
  Complete MCP server exposing 8 Bulk WHOIS API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community

  Import this workflow into your n8n instance
  Credentials Add Bulk WHOIS API credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the Bulk WHOIS API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to http://localhost:5000
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (8 total)

  🔧 Batch (4 endpoints)

  • GET /batch: Get your batches
  • POST /batch: Create batch. Batch is then being processed until all provided items have been completed. At any time it can be get to provide current status with results optionally.
  • DELETE /batch/{id}: Delete batch
  • GET /batch/{id}: Get batch

  🔧 Db (1 endpoints)

  • GET /db: Query domain database

  🔧 Domains (3 endpoints)

  • GET /domains/{domain}/check: Check domain availability
  • GET /domains/{domain}/rank: Check domain rank (authority).
  • GET /domains/{domain}/whois: WHOIS query for a domain

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native Bulk WHOIS API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool"]

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metadata_json:
  {"categories":["DevOps","Data Processing","AI/ML"],"complexity":"complex","use_cases":["Exposing WHOIS search","Domain information retrieval","Automating domain lookups","Bulk domain data gathering","Managing domain records"],"estimated_setup_minutes":5,"required_services":["Bulk WHOIS API","MCP Servers"],"key_features":["Bulk domain querying","AI agent integration","Exposing API operations","MCP server generation"],"target_audience":["AI developer","DevOps engineer","Domain administrator"]}


name: Monitor Software Compliance with Jamf Patch Summaries in Slack
author_name: Jean-Marie Rizkallah
author_username: mrrobot
categories: []
created_at: 2025-07-01T19:28:28.897Z
updated_at: 2025-07-01T19:28:28.897Z
url: https://n8n.io/workflows/5529
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:21
description:
  🧩 Jamf Patch Summary to Slack
  Stay on top of software patch compliance by automatically posting Jamf patch summaries to Slack.
  This helps IT and security teams quickly identify outdated installs and take action—without logging into Jamf.

  ✅ Prerequisites
  • A Jamf Pro API key with permissions to read software titles and patch summary
  • A Slack app or incoming webhook URL with permission to post messages to your desired channel

  🔍 How it works
  • Manually trigger the flow or Add a webhook
  • Fetch a list of software titles from Jamf Pro
  • Filter to select the software you're tracking (e.g. Chrome, Edge)
  • Retrieve the patch summary for that software (latest version, up-to-date, out-of-date counts)
  • Format the summary into Slack Block Kit
  • Post the formatted summary into a Slack channel

  ⚙️ Set up steps
  • Takes ~5–10 minutes to configure
  • Set your server BaseURL variable in the Set Node
  • Add your Jamf Pro API credentials in the HTTP Request nodes (Get & Retrieve)
  • Set the target software ID in the Filter node
  • Add your Slack webhook URL or token in the final HTTP node
  • Optional: Adjust Slack formatting inside the Function node

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.filter","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.slack"]

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metadata_json:
  {"categories":["Operations","IT & Security","Compliance Monitoring"],"complexity":"medium","use_cases":["post Jamf patch summaries to Slack","track software updates","monitor device compliance","automate security alerts"],"estimated_setup_minutes":25,"required_services":["Jamf Pro","Slack"],"key_features":["automatic status reporting","real-time notifications","manual triggering","modular node design","message formatting"],"target_audience":["IT administrators","security analysts","operations engineers"]}


name: Auto Generate & Post LinkedIn Content for Amazon Sellers with GPT-4o & Apify
author_name: Usman Liaqat
author_username: usmanliaqat
categories: []
created_at: 2025-07-01T20:30:56.580Z
updated_at: 2025-07-01T20:30:56.580Z
url: https://n8n.io/workflows/5531
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:21
description:
  How it works

  This powerful automation turns LinkedIn influencer activity into high performing Amazon seller content, fully hands-off. It runs in two phases:

  AI Content Generator
     Retrieves influencer usernames from Airtable
     Scrapes their most recent LinkedIn post via Apify
     Uses OpenAI (GPT-4o) to generate original Amazon focused post content
     Generates a branded LinkedIn image using AI (WeSpark visual identity)
     Saves content & image link to Google Drive
     Creates a record in Airtable with Status = In review

  Scheduled Publisher

     Every day at 4 PM, one post from Airtable with Status = Approved is:

       Downloaded from Drive
       Published on LinkedIn
       Marked as Posted in Airtable

  ⚙️ Set Up Steps

  Time to set up:** 15–20 minutes
  Create two Airtable tables:

    influencers with UserName (LinkedIn usernames only)
    posts with fields:

      Description (text)
      Image (Drive link)
      InfluencerUsername
      InfluencerPost
      InfluencerPostCreatedAt
      Status (In review, Approved, Posted, Rejected)
  Add Google Drive OAuth2 credentials for saving & downloading images
  Configure OpenAI GPT-4o key (for text + image generation)
  Set up Apify LinkedIn scraper and add header credentials
  Link your LinkedIn account (company or personal profile)

  Workflow Phases

  1. AI Generator (Triggered manually)

  Loops over influencer usernames
  Scrapes latest LinkedIn post
  Skips if already stored
  Uses GPT-4o to:

    Rewrite the post uniquely for Amazon sellers
    Generate JSON with text + image prompt
  Saves AI-generated image to Google Drive
  Logs post to Airtable with In review status

  2. Daily Publisher (Scheduled)

  Runs every day at 4 PM
  Filters Airtable for Status = Approved
  Picks 1 record, downloads image from Drive
  Publishes to LinkedIn
  Updates Airtable status to Posted

  Airtable Setup Example

  influencers Table:

  | UserName    |
  | ----------- |
  | jon-derkits |

  posts Table:

  | Description                    | Image         | InfluencerUsername | InfluencerPost  | InfluencerPostCreatedAt | Status    |
  | ------------------------------ | ------------- | ------------------ | --------------- | ----------------------- | --------- |
  | Amazon Listing Optimization... | \[Drive Link] | jon-derkits        | \[LinkedIn URL] | 2025-06-18 19:30:01     | In review |

  Status field options:

  In review
  Approved
  Posted
  Rejected

  Example LinkedIn Visuals

  | Caption                                                                                            | Image                                                                                               |
  | -------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------- |
  | Boost Your Amazon ROI!  Data-driven strategies are your secret weapon...                    | View Image |
  | Boost Sales with 5–10% Higher Conversions!  Utilize Enhanced Brand Content and Smart PPC... | View Image |
  | Grow Your Amazon Business  WeSpark – Amazon Consulting Agency...                            | View Image |
  | Amazon Growth Strategies  Embrace the power of smart optimization...                     | View Image |

  ✅ Best For:

  Amazon agencies and FBA consultants
  B2B SaaS tools targeting eCommerce
  LinkedIn ghostwriters and creators
  Content marketing teams for Amazon sellers

nodes_used:
  ["@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.airtable","n8n-nodes-base.splitInBatches","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.airtable","n8n-nodes-base.if","n8n-nodes-base.googleDrive","n8n-nodes-base.airtable","n8n-nodes-base.airtable","n8n-nodes-base.limit","n8n-nodes-base.googleDrive","n8n-nodes-base.linkedIn","n8n-nodes-base.airtable","n8n-nodes-base.scheduleTrigger"]

workflow_json_compressed:
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metadata_json:
  {"categories":["AI/ML","Marketing","Data Processing"],"complexity":"medium","use_cases":["Generate social media posts","Post to LinkedIn","Monitor LinkedIn influencers","Automate content creation","Create branded images"],"estimated_setup_minutes":45,"required_services":["Apify","OpenAI","Google Drive","Airtable","LinkedIn"],"key_features":["AI-powered content generation","Social media scraping","Image generation","Cross-platform integration","Hands-off automation"],"target_audience":["Amazon sellers","Content creators","Marketing managers"]}


name: Real-time PulsePoint Emergency Alerts to iMessage with AI Summaries
author_name: David Harvey
author_username: xtgy
categories: []
created_at: 2025-07-01T20:58:26.415Z
updated_at: 2025-07-01T20:58:26.415Z
url: https://n8n.io/workflows/5533
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:21
description:
  🚨 Emergency Alerts Reporter to iMessage

  This n8n template fetches real-time emergency incident alerts from PulsePoint for a specific agency and delivers them directly to any phone number via iMessage using the Blooio API. It's designed to keep users informed with clear, AI-summarized reports of emergency activity near them—automatically and reliably.

  Use cases are powerful and immediate:  
  Get real-time fire/medical alerts for your neighborhood.  
  Use it for family, local safety groups, or even emergency response teams.  
  Convert technical dispatch data into readable updates with emojis and plain English.

  🧠 Good to Know

  You’ll need a PulsePoint agency ID (see instructions below).  
  iMessages are sent using Blooio’s API (which supports Apple’s iMessage and fallback RCS/SMS).  
  Messages are AI-enhanced using OpenAI's o4-mini model to summarize incident reports with context and urgency.  
  The workflow runs every hour, but this can be configured to match your needs.  
  Each report is sent only once, thanks to persistent tracking of seen incident IDs in workflow static memory.

  ⚙️ How it Works

  Trigger: A Schedule Trigger (every hour) or manual start kicks off the flow.  
  Get Alerts: A code node fetches the latest PulsePoint incidents for a specified agency and decrypts the data.  
  Filter New Incidents: We store previously seen incident IDs to prevent duplicate alerts.  
  Merge Incidents: All new incident details are merged into a single payload.  
  Condition Check: If there are no new incidents, nothing is sent.  
  AI Summary: The incident data is passed to an AI agent for summarization with human-friendly emojis and formatting.  
  Send Message: The final summary is sent via Blooio’s API to your phone using iMessage.

  📝 How to Use

  Get Your PulsePoint Agency ID:  
     Visit https://web.pulsepoint.org.  
     Find your agency by location or name.  
     Inspect the API call or browser network log to get the agencyid (e.g. 19100 from a URL like ?agencyid=19100).  

  Set Up Blooio for Messaging:  
     Sign up at https://blooio.com.  
     Go to your account and retrieve your Bearer API Key.  
     Pricing details available on their pricing page.  
     Add your key to the HTTP Request node as a Bearer Token.  

  OpenAI API:  
     Create or use an existing OpenAI account.  
     Use the o4-mini model for efficient, readable summaries.  
     Get your OpenAI API key from https://platform.openai.com/account/api-keys.  

  Add Your Phone Number:  
     Replace +1111112222 with your actual number (international format).  
     You can also modify the message content or prepend special tags/emojis.

  ✅ Requirements

  PulsePoint agency ID** – See usage instructions above  
  OpenAI API Key** – Get API Key  
  Blooio Account & Bearer Token** – Get Started  
  Phone number** for iMessage delivery  

  🔧 Customizing This Workflow

  Change the schedule** to get alerts more or less frequently  
  Add filters** to only get alerts for specific incident types (e.g. fires, traffic accidents)  
  Send to groups**: Expand to send alerts to multiple recipients or use Slack instead of iMessage  
  Use different AI prompts** to get detailed, humorous, or abbreviated alerts depending on your audience  

  With just a few credentials and a phone number, you’ll have real-time incident alerts with human-friendly summaries at your fingertips.

  🛠️ Stay informed. Stay safe.

nodes_used:
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name: eBay Finances Data Access for AI Agents with MCP Server Integration
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-01T21:05:51.006Z
updated_at: 2025-07-01T21:05:51.006Z
url: https://n8n.io/workflows/5534
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator?

  Join the community

  Complete MCP server exposing 7 eBay Finances API operations to AI agents.

  ⚡ Quick Setup

  Import this workflow into your n8n instance
  Credentials Add eBay Finances API credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the eBay Finances API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://apiz.ebay.com{basePath}
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (7 total)

  🔧 Payout

  • GET /payout: List seller payouts with filters
  • GET /payout/{payout_Id}: Get single payout details
  🔧 Payout_Summary

  • GET /payout_summary: Show payout totals by status
  🔧 Seller_Funds_Summary

  • GET /seller_funds_summary: View pending/unreleased funds
  🔧 Transaction

  • GET /transaction: Search monetary transactions
  🔧 Transaction_Summary

  • GET /transaction_summary: Get transaction totals + holds
  🔧 Transfer

  • GET /transfer/{transfer_Id}: Retrieve eBay reimbursement details

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native eBay Finances API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

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metadata_json:
  {"categories":["AI/ML","Data Processing","E-commerce"],"complexity":"medium","use_cases":["Enable AI agents to access eBay finances data via MCP server","Expose eBay API operations for external AI tools","Integrate e-commerce financial data with autonomous agents"],"estimated_setup_minutes":10,"required_services":["eBay Finances API"],"key_features":["MCP server configuration for AI agents","7 eBay Finances API operation exposures","Webhook-based trigger mechanism"],"target_audience":["AI developers","e-commerce integrators","automation engineers"]}


name: Internet Archive Search API Integration for AI Agents (3 Operations)
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-01T21:17:27.764Z
updated_at: 2025-07-01T21:17:27.764Z
url: https://n8n.io/workflows/5535
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:21
description:
  Complete MCP server exposing 3 Search Services API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community

  Import this workflow into your n8n instance
  Credentials Add Search Services credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the Search Services API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://api.archive.org
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (3 total)

  🔧 Search (3 endpoints)

  • GET /search/v1/fields: Fields that can be requested
  • GET /search/v1/organic: Return relevance-based results from search queries

  • GET /search/v1/scrape: Scrape search results from Internet Archive, allowing a scrolling cursor

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native Search Services API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool"]

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metadata_json:
  {"categories":["AI/ML"],"complexity":"medium","use_cases":["search data for AI agents","access archive materials programmatically","integrate MCP server with LLMs"],"estimated_setup_minutes":15,"required_services":["Search Services API","Internet Archive"],"key_features":["MCP server implementation","webhook authentication","API search operations"],"target_audience":["AI developers","researchers"]}


name: Internet Archive Wayback Machine API for AI Assistants
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-01T21:18:56.407Z
updated_at: 2025-07-01T21:18:56.407Z
url: https://n8n.io/workflows/5536
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Complete MCP server exposing 2 Wayback API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community
  Import this workflow into your n8n instance
  Credentials Add Wayback API credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the Wayback API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://api.archive.org
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (2 total)

  🔧 Wayback (2 endpoints)

  • GET /wayback/v1/available: GET /wayback/v1/available
  • POST /wayback/v1/available: POST /wayback/v1/available

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native Wayback API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool"]

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metadata_json:
  {"categories":["AI/ML","Data Processing"],"complexity":"medium","use_cases":["Retrieve historical web data for AI agents","Archive access via Wayback Machine API","Integrate Wayback API with LLMs","Build custom MCP servers for web archives"],"estimated_setup_minutes":10,"required_services":["Internet Archive Wayback Machine","MCP Server"],"key_features":["Direct Wayback API integration into AI assistants","MCP Server configuration for agent access","Webhook-based trigger mechanism","Historical page retrieval capabilities"],"target_audience":["AI Developers","Data Scientists","ML Engineers"]}


name: IP Geolocation Lookup with BigDataCloud API for AI Agents
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-01T21:27:24.500Z
updated_at: 2025-07-01T21:27:24.500Z
url: https://n8n.io/workflows/5539
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Complete MCP server exposing 2 IP Geolocation API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community
  Import this workflow into your n8n instance
  Credentials Add IP Geolocation API credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the IP Geolocation API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://api.bigdatacloud.net
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (2 total)

  🔧 Data (2 endpoints)

  • GET /data/ip-geolocation-full: IP Geolocation with Confidence Area and Hazard Report API
  • GET /data/ip-geolocation-with-confidence: IP Geolocation with Confidence Area API

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native IP Geolocation API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool"]

workflow_json_compressed:
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metadata_json:
  {"categories":["AI/ML"],"complexity":"simple","use_cases":["lookup IP location","enhance AI agents","enrich agent context"],"estimated_setup_minutes":10,"required_services":["BigDataCloud","HTTP","MCP Server"],"key_features":["MCP server protocol support","AI agent integration","IP geolocation endpoint","webhook endpoint","credential management"],"target_audience":["developers","AI engineers"]}


name: Query Bicycle Incident Data with BikeWise API through MCP Server
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-01T21:28:34.770Z
updated_at: 2025-07-01T21:28:34.770Z
url: https://n8n.io/workflows/5540
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Complete MCP server exposing 4 BikeWise API v2 API operations to AI agents.

  ⚡ Quick Setup
  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community

  Import this workflow into your n8n instance
  Credentials Add BikeWise API v2 credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the BikeWise API v2 API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://bikewise.org/api
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (4 total)

  🔧 V2 (4 endpoints)

  • GET /v2/incidents: Paginated incidents matching parameters
  • GET /v2/incidents/{id}: GET /v2/incidents/{id}
  • GET /v2/locations: Unpaginated geojson response
  • GET /v2/locations/markers: Unpaginated geojson response with simplestyled markers

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native BikeWise API v2 API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 4108 characters]

metadata_json:
  {"categories":["Data Processing","AI/ML","API Integration"],"complexity":"medium","use_cases":["Access bicycle incident data for AI agents","Expose BikeWise API v2 to AI workflows","Query incident records via MCP server"],"estimated_setup_minutes":5,"required_services":["BikeWise API v2"],"key_features":["MCP server implementation","AI agent compatibility","HTTP/Webhook integration"],"target_audience":["developers","ML engineers","API users"]}


name: Track AI Agent token usage and estimate costs in Google Sheets
author_name: Solomon
author_username: solomon
categories: []
created_at: 2025-07-01T21:30:24.313Z
updated_at: 2025-07-01T21:30:24.313Z
url: https://n8n.io/workflows/5541
scraped_at: 2026-05-02 08:04:48
metadata_generated_at: 2026-05-02 08:36:20
description:
  This n8n template demonstrates how to obtain token usage from AI Agents and places the data into a spreadsheet that calculates the estimated cost of the execution.

  Obtaining the token usage from AI Agents is tricky, because it doesn't provide all the data from tool calls. This workflow taps into the workflow execution metadata to extract token usage information.

  Works well with OpenAI, Google and Anthropic. Other LLM providers might need small tweaks.

  How it works
  The AI Agent executes and then calls a subworkflow to calculate the token usage.
  The data is stored in Google Sheets
  The spreadsheet has formulas to calculate the estimated cost of the execution.

  How to use
  The AI Agent is used as an example. Feel free to replace this with other agents you have.
  Call the subworkflow AFTER all the other branches have finished executing.

  Requirements
  LLM account (OpenAI, Gemini...) for API usage.
  Google Drive and Sheets credentials
  n8n API key of your instance

nodes_used:
  ["n8n-nodes-base.manualTrigger","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.executeWorkflow","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.toolThink","n8n-nodes-base.set","n8n-nodes-base.n8n","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.splitOut","n8n-nodes-base.summarize","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatAnthropic","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 6388 characters]

metadata_json:
  {"categories":["AI/ML","Data Processing"],"complexity":"complex","use_cases":["Monitor LLM token consumption","Calculate AI operational costs","Track agent execution metrics"],"estimated_setup_minutes":15,"required_services":["Google Sheets","OpenAI","Google AI Studio (Gemini)","Anthropic"],"key_features":["Extract token usage from workflow metadata","Handle tool call data gaps","Multi-provider LLM compatibility","Real-time cost estimation"],"target_audience":["LLM engineers","AI operations managers"]}


name: BIN Card Information Lookup API Connector for AI Agents with Balance Check
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-01T21:39:31.341Z
updated_at: 2025-07-01T21:39:31.341Z
url: https://n8n.io/workflows/5542
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Complete MCP server exposing 2 BIN Lookup API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community

  Import this workflow into your n8n instance
  Credentials Add BIN Lookup API credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the BIN Lookup API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://api.bintable.com/v1
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (2 total)

  🔧 Balance (1 endpoints)

  • GET /balance: Check Balance

  🔧 {Bin} (1 endpoints)

  • GET /{bin}: Lookup for bin

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native BIN Lookup API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool"]

workflow_json_compressed:
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metadata_json:
  {"categories":["Data Processing"],"complexity":"medium","use_cases":["Fetch BIN details","Validate card numbers","Check account types","Integrate with AI agents"],"estimated_setup_minutes":5,"required_services":["BIN Lookup API (Maitria)"],"key_features":["MCP server setup","BIN validation","Account type detection","AI agent compatibility"],"target_audience":["developers","data analysts"]}


name: Create Evergreen Content with GitHub Dynamic Images & URL Redirects
author_name: Eduard
author_username: eduard
categories: []
created_at: 2025-07-01T23:04:05.588Z
updated_at: 2025-07-01T23:04:05.588Z
url: https://n8n.io/workflows/5546
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:21
description:
  Transform static digital assets into dynamic, self-updating powerhouses that stay relevant for years to come!

  This workflow solves a common problem: once you publish forms, emails, or templates, their content becomes frozen in time. Users discovering them months later see outdated information, missed opportunities, and stale offers.

  Stop losing opportunities to stale content – make your digital assets work harder and stay fresher, automatically!

  Here's how it works:
  🔗 Stable embed links mean your original assets never need updating
  🔄 Dynamic URL redirects that automatically point to the latest pages
  🖼️ Auto-updating images that showcase fresh offers or content
  📅 Scheduled updates keep everything current without manual intervention

  Perfect for:
  Workflow sticky notes that become evergreen marketing billboards
  Registration forms with current promotions
  Email signatures with latest offers  
  Website banners that stay seasonally relevant
  Any digital asset you want to "future-proof"

  The magic: Set it up once, embed the stable URLs/images in your content, then forget about it. Years later, users will still see fresh, as current information automatically pulled from your workflow.

  Requirements: Free accounts with GitHub (image storage) and shorten.rest  (URL redirects).

  Both can be swapped for your preferred services.

  Follow me on LinkedIn for more tips on AI automation and n8n workflows!

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.editImage","n8n-nodes-base.github","n8n-nodes-base.stickyNote","n8n-nodes-base.formTrigger","n8n-nodes-base.github","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote","n8n-nodes-base.github","n8n-nodes-base.github","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["Content Creation","Digital Asset Management","Marketing Automation"],"complexity":"medium","use_cases":["auto updating static assets","preserving embedded links stability","refreshing outdated content automatically","managing dynamic digital forms","generating fresh email templates"],"estimated_setup_minutes":45,"required_services":["GitHub"],"key_features":["creates evergreen content automatically","maintains stable embed links","prevents content aging","replaces outdated static assets","schedules regular content refreshes"],"target_audience":["content managers","marketing teams","tech-savvy creators"]}


name: SMS Messaging API Integration for AI Agents with BulkSMS
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-01T23:46:43.610Z
updated_at: 2025-07-01T23:46:43.610Z
url: https://n8n.io/workflows/5548
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:21
description:
  Complete MCP server exposing 15 BulkSMS JSON REST API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community

  Import this workflow into your n8n instance
  Credentials Add BulkSMS JSON REST API credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the BulkSMS JSON REST API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://api.bulksms.com/v1
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (15 total)

  🔧 Blocked-Numbers (2 endpoints)

  • GET /blocked-numbers: Block Phone Number
  • POST /blocked-numbers: Create a blocked number

  🔧 Credit (1 endpoints)

  • POST /credit/transfer: Transfer Account Credits

  🔧 Messages (5 endpoints)

  • GET /messages: List Related Messages
  • POST /messages: Send Messages
  • GET /messages/send: Send message by simple GET or POST
  • GET /messages/{id}: Show Message
  • GET /messages/{id}/relatedReceivedMessages: List Related Messages

  🔧 Profile (1 endpoints)

  • GET /profile: Retrieve User Profile

  🔧 Rmm (1 endpoints)

  • POST /rmm/pre-sign-attachment: Generate Attachment Upload URL

  🔧 Webhooks (5 endpoints)

  • GET /webhooks: Update Webhook Settings
  • POST /webhooks: Create a webhook
  • DELETE /webhooks/{id}: Delete a webhook
  • GET /webhooks/{id}: Read a webhook
  • POST /webhooks/{id}: Update a webhook

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native BulkSMS JSON REST API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool"]

workflow_json_compressed:
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metadata_json:
  {"categories":["AI/ML","Communication"],"complexity":"medium","use_cases":["integrate bulk SMS APIs","expose API to AI agents","build MCP server","extend AI agent capabilities"],"estimated_setup_minutes":5,"required_services":["BulkSMS JSON REST API"],"key_features":["MCP server implementation","BulkSMS API integration","HTTP/Webhook-based interaction","AI agent visibility"],"target_audience":["AI developers","integration engineers","devops engineers"]}


name: Access CO2 Measurement Data with CarbonDoomsDay API for AI Agents
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-01T23:49:13.065Z
updated_at: 2025-07-01T23:49:13.065Z
url: https://n8n.io/workflows/5550
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Complete MCP server exposing 2 CarbonDoomsDay API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community

  Import this workflow into your n8n instance
  Credentials Add CarbonDoomsDay credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the CarbonDoomsDay API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://api.carbondoomsday.com/api
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (2 total)

  🔧 Co2 (2 endpoints)

  • GET /co2/: Get CO2 Measurement by Date
  • GET /co2/{date}/: CO2 measurements from the Mauna Loa observatory.

  This data is made available through the good work of the people at the
  Mauna Loa observatory. Their release notes say:

      These data are made freely available to the public and the scientific
      community in the belief that their wide dissemination will lead to greater
      understanding and new scientific insights.

  We currently scrape the following sources:

    [co2_mlo_weekly.csv]
    [co2_mlo_surface-insitu_1_ccgg_DailyData.txt]
    [weekly_mlo.csv]

  We have daily CO2 measurements as far back as 1958.

  Learn about using pagination via [the 3rd party documentation].

  [co2_mlo_weekly.csv]: https://www.esrl.noaa.gov/gmd/webdata/ccgg/trends/co2_mlo_weekly.csv
  [co2_mlo_surface-insitu_1_ccgg_DailyData.txt]: ftp://aftp.cmdl.noaa.gov/data/trace_gases/co2/in-situ/surface/mlo/co2_mlo_surface-insitu_1_ccgg_DailyData.txt
  [weekly_mlo.csv]: http://scrippsco2.ucsd.edu/sites/default/files/data/in_situ_co2/weekly_mlo.csv
  [the 3rd party documentation]: http://www.django-rest-framework.org/api-guide/pagination/#pagenumberpagination

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native CarbonDoomsDay API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool"]

workflow_json_compressed:
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metadata_json:
  {"categories":["Environmental Data","AI/ML"],"complexity":"simple","use_cases":["access CarbonDoomsDay API","expose API operations to AI agents","deploy MCP server"],"estimated_setup_minutes":10,"required_services":["CarbonDoomsDay API"],"key_features":["generates MCP server for CarbonDoomsDay API","connects AI agents to environmental data","webhook-driven setup"],"target_audience":["AI developers","data scientists","environmental analysts"]}


name: AI Personal Assistant with GPT-4o: Email, Calendar, Search & CRM Integration
author_name: Jordan Lee
author_username: jordanlee
categories: []
created_at: 2025-07-01T23:56:27.537Z
updated_at: 2025-07-01T23:56:27.537Z
url: https://n8n.io/workflows/5552
scraped_at: 2026-05-02 08:04:48
metadata_generated_at: 2026-05-02 08:36:20
description:
  This n8n template demonstrates how to use AI as a comprehensive personal assistant with multiple specialized agents.

  Use cases include email management, scheduling, web search, calculations, and more - all automated through AI coordination.

  Good to know
  This template integrates multiple AI services through OpenRouter

  Each agent specializes in different tasks (Gmail, Calendar, Search, etc.)

  Memory persistence maintains context across interactions

  How it works

  The workflow is triggered by Telegram messages (can be replaced with other triggers)

  A router node directs requests to the appropriate specialized agent

  Agents include:

  Gmail for email management

  Calculator for math operations

  Google Search for information retrieval

  Calendar for scheduling

  Contacts for CRM functions

  The OpenRouter Chat Model coordinates responses

  Final responses are sent back through Telegram

  How to use
  Connect your Telegram bot credentials

  Configure each service with appropriate API keys

  The system will automatically route requests to the right agent

  Requirements

  OpenRouter account for AI services

  Telegram bot token

  Google API credentials for relevant services

  Customising this workflow
  Add more specialized agents as needed

  Replace Telegram with other communication channels

  Adjust routing logic for different use cases

nodes_used:
  ["@n8n/n8n-nodes-langchain.toolCalculator","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolSerpApi","n8n-nodes-base.telegram","n8n-nodes-base.telegramTrigger","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.lmChatOpenRouter","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.perplexityTool"]

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metadata_json:
  {"categories":["AI/ML","Automation"],"complexity":"medium","use_cases":["automated email management","calendar scheduling","web search","crm updates","personal assistant tasks"],"estimated_setup_minutes":15,"required_services":["OpenRouter","Gmail API","Google Calendar API","SerpApi","Telegram API"],"key_features":["multi-agent system","context memory persistence","specialized AI tools","telebot integration"],"target_audience":["power users","admins","developers"]}


name: Automate ASMR Video Creation and Distribution with FalAI and Social Media Integration
author_name: Harrison P
author_username: purhar
categories: []
created_at: 2025-07-02T00:02:39.571Z
updated_at: 2025-07-02T00:02:39.571Z
url: https://n8n.io/workflows/5553
scraped_at: 2026-05-02 08:04:48
metadata_generated_at: 2026-05-02 08:36:20
description:
  🎬 Create Unlimited Viral ASMR Videos — Fully Automated!
  Tired of spending hours brainstorming viral content, writing prompts, and juggling video uploads?

  This powerful n8n workflow automates your entire short-form ASMR video creation process, turning you into a content powerhouse — without burnout or repetitive manual work.

  ✅ No coding required
  ✅ Connects with cutting-edge AI video generation (FalAI)
  ✅ Publishes videos to TikTok, Instagram, YouTube, and more
  ✅ Tracks everything neatly in Google Sheets
  ✅ Ready for customization!

  ✨ How It Works
  Here’s how this workflow becomes your virtual content assistant:

  Brainstorm New Viral ASMR Objects
  Generates a creative, unused idea like:

  Glass Kiwi
  Crystal Banana
  Frozen Watermelon
  Ensures fresh content by checking your Google Sheet for duplicates.

  Generate a Detailed Video Prompt
  Transforms your object idea into a sensory-rich ASMR video script:


  “Imagine a crystal-clear kiwi sliced open, glistening under neon lights…”
  This prompt is crafted to produce hyper-realistic AI visuals perfect for short-form platforms.

  Create the ASMR Video with FalAI
  Sends the prompt to FalAI, an advanced video-generation API.

  Supports vertical aspect ratio for social reels.

  Automatically waits for video processing to complete.

  Save Video Details to Google Sheets
  Adds each new object and video URL to a Google Sheet.

  Maintains a running archive of all your generated videos.

  No more manual tracking or losing track of which videos you’ve already created!

  Upload to Blotato and Publish Across Platforms
  Uploads the video to Blotato’s media hosting service.

  Optionally posts the video automatically to:

  TikTok

  Instagram

  YouTube

  And easily extendable to Facebook, Pinterest, and more!

  Posts your captions, hashtags, and links seamlessly.

  🎯 Why You’ll Love This Workflow
  ✅ Saves hours of creative work and publishing time
  ✅ Eliminates manual uploads and spreadsheets
  ✅ Keeps your content fresh and consistent
  ✅ Highly customizable for new platforms or personal style
  ✅ Includes clear instructions and sticky notes for easy setup

  💬 Need Help?
  If you get stuck:

  Check the included sticky notes in the workflow canvas

  Or search for “N8N Assistant by Nskha” on ChatGPT, and upload your JSON file for personalized support.

  🎉 Supercharge Your Content Creation
  This isn’t just a workflow — it’s a full-scale ASMR content machine ready to scale your viral video production effortlessly. Automate your creativity and free up your time to focus on what matters most!

  ✅ Ready to impress your audience and grow your brand?
  Import this workflow today and start creating viral ASMR magic!

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.googleSheets","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.aggregate","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.wait","n8n-nodes-base.wait"]

workflow_json_compressed:
  [encoded data, 8924 characters]

metadata_json:
  {"categories":["Marketing","AI/ML"],"complexity":"medium","use_cases":["generate viral ASMR video scripts","create AI-generated short videos","automate social media posting","track content performance metrics","schedule content across platforms"],"estimated_setup_minutes":25,"required_services":["FalAI","Google Sheets","TikTok API","Instagram Graph API","YouTube Data API","Pan API","X (Twitter) API"],"key_features":["cross-platform video distribution","AI-generated script creation","automated social media scheduling","content performance tracking","multi-formatted video output"],"target_audience":["content creators","social media managers","marketing teams"]}


name: File Hash Verification for AI Agents with hashlookup CIRCL API
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-02T00:05:28.154Z
updated_at: 2025-07-02T00:05:28.154Z
url: https://n8n.io/workflows/5554
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Complete MCP server exposing 11 hashlookup CIRCL API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community
  Import this workflow into your n8n instance
  Credentials Add hashlookup CIRCL API credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the hashlookup CIRCL API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://hashlookup.circl.lu
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (11 total)

  🔧 Bulk (2 endpoints)

  • POST /bulk/md5: Bulk Search MD5 Hashes
  • POST /bulk/sha1: Bulk Search SHA1 Hashes

  🔧 Children (1 endpoints)

  • GET /children/{sha1}/{count}/{cursor}: Return children from a given SHA1.  A number of element to return and an offset must be given. If not set it will be the 100 first elements. A cursor must be given to paginate over. The starting cursor is 0.

  🔧 Info (1 endpoints)

  • GET /info: Get Database Info

  🔧 Lookup (3 endpoints)

  • GET /lookup/md5/{md5}: Lookup MD5.
  • GET /lookup/sha1/{sha1}: Lookup SHA-1.
  • GET /lookup/sha256/{sha256}: Lookup SHA-256.

  🔧 Parents (1 endpoints)

  • GET /parents/{sha1}/{count}/{cursor}: Return parents from a given SHA1. A number of element to return and an offset must be given. If not set it will be the 100 first elements. A cursor must be given to paginate over. The starting cursor is 0.

  🔧 Session (2 endpoints)

  • GET /session/create/{name}: Create a session key to keep search context. The session is attached to a name. After the session is created, the header hashlookup_session can be set to the session name.
  • GET /session/get/{name}: Return set of matching and non-matching hashes from a session.

  🔧 Stats (1 endpoints)

  • GET /stats/top: Get Top Queries

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native hashlookup CIRCL API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool"]

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metadata_json:
  {"categories":["DevOps","Security","AI/ML"],"complexity":"medium","use_cases":["Expose shared MCP servers","Verify file hashes","Integrate hash services into AI agents"],"estimated_setup_minutes":15,"required_services":["CIRCL","hashlookup"],"key_features":["Consume multiple API endpoints","Expose APIs via MCP server protocol","Integrate security hashing services","Webhook-based activation"],"target_audience":["AI developers","DevOps engineers","security officers"]}


name: Business Lead Generation with Apify Web Scraping and Google Sheets Storage
author_name: Jordan Lee
author_username: jordanlee
categories: []
created_at: 2025-07-02T00:06:32.693Z
updated_at: 2025-07-02T00:06:32.693Z
url: https://n8n.io/workflows/5555
scraped_at: 2026-05-02 08:04:48
metadata_generated_at: 2026-05-02 08:36:21
description:
  This flexible template scrapes business listings for any industry and location, perfect for sales teams, marketers, and researchers.

  Good to know
  Works with any business category (restaurants, contractors, retailers, etc.)
  Fully customizable search parameters
  Results automatically organized in Google Sheets
  Built-in delay ensures scraping completes before data collection

  How it works

  Trigger: Manual or scheduled start
  Apify Configuration: Sets scraping parameters (industry, location, data fields)
  Scraping Execution: Runs the web scraping job
  Data Processing: Cleans and structures the raw data
  Storage: Saves results to your Google Sheets

  What is Apify?
  Apify is a webscraping tool, in this workflow the data is scraped from a google maps scraper:

  https://apify.com/compass/crawler-google-places

  How to use

  Apify Small # Lead Generation (Purple)

  https://apify.com/compass/crawler-google-places


  Add location and industry to scrape (Apify)
  Add the number of leads to output (Apify)
  Copy over the JSON file into N8N
  Copy & paste API endpoint "Get Run URL" in N8N

  Apify Large # Lead Generation (Grey)

  Configure the Manual Trigger

  When clicking 'Execute workflow' node is ready to use as-is
  This triggers the entire lead generation process

  Setup "Start Results (Apify)" Node
  Get Your Apify API Information

  Go to Apify.com and create a free account
  Navigate to Settings → Integrations → API tokens
  Copy your API token
  Find the Google Maps scraper actor ID:

  Configure the HTTP Request (start results)

  Method: POST
  URL: Replace "enter apify (get run)" with:
  https://api.apify.com/v2/acts/nwua9Gu5YrADL7ZDj/runs?token=YOUR_API_TOKEN


  C. Customize the JSON Body Parameters
  In the JSON body, modify these key fields:
  Location & Search:

  "locationQuery": Change "Toronto" to your target city
  "searchStringsArray": Change ["barber"] to your business type

  Examples: ["restaurants"], ["dentists"], ["contractors"]

  Configure the HTTP Request (start results)
  Method : Get
  Url: enter the get dataset URL from Apify

  Split Out Node
  Select fields to append in the google sheet

  Test the Configuration

  Click Execute workflow to test
  Check that the Apify job starts successfully
  Note the job ID returned for the next section

  This section initiates the scraping process and should complete in 30-60 seconds depending on your lead count.

  Setup Google Sheets

  Create a new Google Sheet with these columns:

  title (business name)
  address (full address)
  state (state/province)
  neighborhood (area/district)
  phone (contact number)
  emails (email addresses)


  Copy your Google Sheets document ID for workflow configuration

  Requirements
  Apify account
  Google Sheets document
  Google OAuth credentials

  Customization Options
  For different use cases:

  Lead Gen: Get business leads
  Local SEO: Collect competitor data
  Market Research: Analyze industry trends

  Advanced mofications:
  Add email enrichment
  Integrate with CRM systems
  Set up automatic daily runs

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.wait","n8n-nodes-base.splitOut","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest"]

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metadata_json:
  {"categories":["Data Processing","Marketing","Business Intelligence"],"complexity":"medium","use_cases":["scrape business listings","collect industry contacts","store data in spreadsheets","generate sales leads","research competitors"],"estimated_setup_minutes":5,"required_services":["Apify","Google Sheets","n8n"],"key_features":["configurable search parameters","scheduled scraping","industry-agnostic scraping","automatic data organization","built-in delay for scraping targets"],"target_audience":["sales teams","marketers","researchers"]}


name: Audio & Video Data Search and Analysis with Clarify API and AI Agent Integration
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-02T00:13:52.614Z
updated_at: 2025-07-02T00:13:52.614Z
url: https://n8n.io/workflows/5557
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:21
description:
  Complete MCP server exposing 21 api.clarify.io API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community

  Import this workflow into your n8n instance
  Credentials Add api.clarify.io credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the api.clarify.io API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://api.clarify.io/
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (21 total)

  🔧 V1 (21 endpoints)

  • GET /v1/bundles: Add Media to Track
  • POST /v1/bundles: Create a bundle
  • DELETE /v1/bundles/{bundle_id}: Delete a bundle
  • GET /v1/bundles/{bundle_id}: Get a bundle
  • PUT /v1/bundles/{bundle_id}: Update a bundle
  • GET /v1/bundles/{bundle_id}/insights: Get bundle insights
  • POST /v1/bundles/{bundle_id}/insights: Request an insight to be run
  • GET /v1/bundles/{bundle_id}/insights/{insight_id}: Get bundle insight
  • DELETE /v1/bundles/{bundle_id}/metadata: Delete bundle metadata
  • GET /v1/bundles/{bundle_id}/metadata: Get bundle metadata
  • PUT /v1/bundles/{bundle_id}/metadata: Update bundle metadata
  • DELETE /v1/bundles/{bundle_id}/tracks: Delete bundle tracks
  • GET /v1/bundles/{bundle_id}/tracks: Get bundle tracks
  • POST /v1/bundles/{bundle_id}/tracks: Add a track for a bundle
  • PUT /v1/bundles/{bundle_id}/tracks: Update a tracks for a bundle
  • DELETE /v1/bundles/{bundle_id}/tracks/{track_id}: Delete a bundle track
  • GET /v1/bundles/{bundle_id}/tracks/{track_id}: Get bundle track
  • PUT /v1/bundles/{bundle_id}/tracks/{track_id}: Add media to a track
  • GET /v1/reports/scores: Generate Group Report
  • GET /v1/reports/trends: Generate Trends Report
  • GET /v1/search: Search Bundles

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native api.clarify.io API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool"]

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metadata_json:
  {"categories":["AI/ML","Data Processing","DevOps"],"complexity":"medium","use_cases":["api integration","mcp server setup","ai agent data access","clarify io operations"],"estimated_setup_minutes":15,"required_services":["Clarify.io API","MCP (Model Context Protocol)"],"key_features":["API exposure","AI agent integration","MCP server creation","webhook trigger","managed credentials"],"target_audience":["AI developers","system architects","integrators"]}


name: Team Knowledge Management with Google Docs, Discord, and AI Assistant
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-02T01:10:26.154Z
updated_at: 2025-07-02T01:10:26.154Z
url: https://n8n.io/workflows/5560
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator?

  Join the community

  Let’s activate your org’s memory:

  📝 Set Storage

  Open "Edit Fields" → Replace sheet_id with your Google Doc URL.

  🤖 Connect Discord

  Update discord_server_id, channel_id, and user_id (for DMs).

  🧠 Route Commands
  Call these from other workflows:
  save_memory: Log new data.
  retrieve_memory: Fetch history.
  send_memories_to_gmail: Discord summaries.

  🤖 Custom Formatting
  Edit the AI Agent prompt to style Discord messages.

  💡 Pro Tip: Pair this with an MCP Server for full issue→memory→action automation!

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community

nodes_used:
  ["n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.googleDocs","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.googleDocs","n8n-nodes-base.switch","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.mcpTrigger","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.googleDocs","n8n-nodes-base.discordTool","n8n-nodes-base.discordTool"]

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metadata_json:
  {"categories":["Data Processing","Communication"],"complexity":"medium","use_cases":["centralize team documentation","automate memory retrieval","integrate chat commands","sync knowledge base to emails"],"estimated_setup_minutes":30,"required_services":["Google Docs","Discord","Gmail","AI/LLM API","Google Sheets API"],"key_features":["unified memory storage","AI-powered knowledge retrieval","chat-based access controls","automated email summaries"],"target_audience":["knowledge managers","developers","ops engineers"]}


name: Expose eBay Browse API for AI Agents with MCP Server
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-02T01:25:43.233Z
updated_at: 2025-07-02T01:25:43.233Z
url: https://n8n.io/workflows/5563
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator?

  Join the community

  Complete MCP server exposing 11 Browse API operations to AI agents.

  ⚡ Quick Setup

  Import this workflow into your n8n instance
  Credentials Add Browse API credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the Browse API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://api.ebay.com/buy/browse/v1
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (11 total)

  🔧 Item (5 endpoints)

  • GET /item/: Check Item Compatibility
  • GET /item/get_item_by_legacy_id: Get Get Item By Legacy Id
  • GET /item/get_items_by_item_group: This method retrieves the details of the individual items in an item group
  • GET /item/{item_id}: Get {Item Id}
  • POST /item/{item_id}/check_compatibility: This method checks if a product is compatible with the specified item

  🔧 Item_Summary (2 endpoints)

  • GET /item_summary/search: Search Items by Image
  • POST /item_summary/search_by_image: This is an Experimental method

  🔧 Shopping_Cart (4 endpoints)

  • GET /shopping_cart/: Update Cart Item Quantity
  • POST /shopping_cart/add_item: This is an Experimental method
  • POST /shopping_cart/remove_item: This is an experimental method
  • POST /shopping_cart/update_quantity: This is an experimental method

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native Browse API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool"]

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metadata_json:
  {"categories":["DevOps"],"complexity":"medium","use_cases":["expose API operations","connect AI agents","build MCP server","automate agent access"],"estimated_setup_minutes":10,"required_services":["eBay Browse API"],"key_features":["MCP server implementation","HTTP/Webhooks integration","AI agent connectivity"],"target_audience":["developers","AI engineers","integration specialists"]}


name: Connect AI Agents to eBay Deal API with MCP Server
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-02T02:30:35.403Z
updated_at: 2025-07-02T02:30:35.403Z
url: https://n8n.io/workflows/5564
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator?

  Join the community

  Complete MCP server exposing 4 Deal API operations to AI agents.

  ⚡ Quick Setup

  Import this workflow into your n8n instance
  Credentials Add Deal API credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the Deal API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://api.ebay.com{basePath}
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (4 total)

  🔧 Deal_Item (1 endpoints)

  • GET /deal_item: List Deal Items

  🔧 Event (2 endpoints)

  • GET /event: List Event Items
  • GET /event/{event_id}: This method retrieves the details for an eBay event

  🔧 Event_Item (1 endpoints)

  • GET /event_item: This method returns a paginated set of event items

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native Deal API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool"]

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metadata_json:
  {"categories":["AI/ML","E-commerce","API Integration"],"complexity":"medium","use_cases":["automate eBay deal discovery","connect AI agents to e-commerce APIs","enable AI tools to query real-time deals"],"estimated_setup_minutes":5,"required_services":["eBay Deal API","MCP Server"],"key_features":["barcode corporation integration","real-time deal synchronization","AI agent interaction via MCP protocol"],"target_audience":["e-commerce managers","developers","AI engineers"]}


name: Expose eBay Taxonomy API to AI Agents for Category Management
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-02T02:36:20.986Z
updated_at: 2025-07-02T02:36:20.986Z
url: https://n8n.io/workflows/5565
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator?

  Join the community

  Complete MCP server exposing 8 Taxonomy API operations to AI agents.

  ⚡ Quick Setup

  Import this workflow into your n8n instance
  Credentials Add Taxonomy API credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the Taxonomy API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://api.ebay.com/commerce/taxonomy/v1
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (8 total)

  🔧 Category_Tree (7 endpoints)

  • GET /category_tree/{category_tree_id}: Get a Category Tree
  • GET /category_tree/{category_tree_id}/fetch_item_aspects: Get Aspects for All Leaf Categories in a Marketplace
  • GET /category_tree/{category_tree_id}/get_category_subtree: Get a Category Subtree
  • GET /category_tree/{category_tree_id}/get_category_suggestions: Get Suggested Categories
  • GET /category_tree/{category_tree_id}/get_compatibility_properties: Get Compatibility Properties
  • GET /category_tree/{category_tree_id}/get_compatibility_property_values: Get Compatibility Property Values
  • GET /category_tree/{category_tree_id}/get_item_aspects_for_category: Get Get Item Aspects For Category

  🔧 Get_Default_Category_Tree_Id (1 endpoints)

  • GET /get_default_category_tree_id: Fetch Default Category Tree ID

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native Taxonomy API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool"]

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metadata_json:
  {"categories":["AI/ML","Data Processing"],"complexity":"medium","use_cases":["categorizing products","managing product hierarchies","enabling AI agents to access taxonomy data","exposing APIs for agent collaboration"],"estimated_setup_minutes":10,"required_services":["Taxonomy API"],"key_features":["MCP server integration","API operation exposure","Webhook trigger support","Credential-based authentication"],"target_audience":["developers","AI engineers","data architects"]}


name: Search and Retrieve eBay Product Data with Catalog API for AI Agents
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-02T02:43:42.731Z
updated_at: 2025-07-02T02:43:42.731Z
url: https://n8n.io/workflows/5566
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:21
description:
  Complete MCP server exposing 2 Catalog API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community

  Import this workflow into your n8n instance
  Credentials Add Catalog API credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the Catalog API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://api.ebay.com{basePath}
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (2 total)

  🔧 Product (1 endpoints)

  • GET /product/{epid}: Get {Epid}

  🔧 Product_Summary (1 endpoints)

  • GET /product_summary/search: Search Product Summaries

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native Catalog API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool"]

workflow_json_compressed:
  [encoded data, 8360 characters]

metadata_json:
  {"categories":["AI/ML","E-commerce"],"complexity":"medium","use_cases":["expose e-commerce catalog API to AI agents","enable AI assistants to search products","connect MCP servers to catalog data sources"],"estimated_setup_minutes":5,"required_services":["eBay Catalog API"],"key_features":["MCP server integration","Catalog API endpoints","Webhook exposure","AI agent connectivity"],"target_audience":["AI developers","e-commerce developers","data engineers"]}


name: AI Agent Integration with eBay Buy Marketing API
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-02T02:46:16.330Z
updated_at: 2025-07-02T02:46:16.330Z
url: https://n8n.io/workflows/5567
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:21
description:
  Complete MCP server exposing 1 Buy Marketing API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community

  Import this workflow into your n8n instance
  Credentials Add Buy Marketing API credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the Buy Marketing API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://api.ebay.com/buy/marketing/v1_beta
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (1 total)

  🔧 Merchandised_Product (1 endpoints)

  • GET /merchandised_product: Fetch Merchandised Products

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native Buy Marketing API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool"]

workflow_json_compressed:
  [encoded data, 4424 characters]

metadata_json:
  {"categories":["Marketing"],"complexity":"medium","use_cases":["expose Buy Marketing API operations to AI agents","setup MCP server for n8n integration","integrate eBay APIs with AI workflows"],"estimated_setup_minutes":15,"required_services":["eBay Buy Marketing API","n8n"],"key_features":["advances marketing data with AI context","enables programmable access to eBay analytics","facilitates MCP server configuration","provides webhook handlers for external triggers"],"target_audience":["developers","marketing analysts","AI engineers"]}


name: Create an eBay Item Feed Service API Gateway for AI Agents
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-02T02:55:10.989Z
updated_at: 2025-07-02T02:55:10.989Z
url: https://n8n.io/workflows/5568
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator?

  Join the community

  Complete MCP server exposing 4 Item Feed Service API operations to AI agents.

  ⚡ Quick Setup

  Import this workflow into your n8n instance
  Credentials Add Item Feed Service credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the Item Feed Service API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://api.ebay.com/buy/feed/v1_beta
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (4 total)

  🔧 Item (1 endpoints)

  • GET /item: Download Hourly Snapshot Feed

  🔧 Item_Group (1 endpoints)

  • GET /item_group: Get Item Group

  🔧 Item_Priority (1 endpoints)

  • GET /item_priority: Get Item Priority

  🔧 Item_Snapshot (1 endpoints)

  • GET /item_snapshot: Get Item Snapshot

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native Item Feed Service API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["Developer Tools","AI/ML","API Integration"],"complexity":"medium","use_cases":["Expose Item Feed Service to AI agents","Create MCP server gateway","Enable AI agent API access","Secure API operations for agents"],"estimated_setup_minutes":15,"required_services":["Item Feed Service API"],"key_features":["MCP server creation","API operation exposure","Webhook integration","Credential management","AI agent connectivity"],"target_audience":["developers","AI/ML engineers","integration specialists"]}


name: eBay Analytics API Rate Limit Monitoring for AI Agents
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-02T03:41:47.580Z
updated_at: 2025-07-02T03:41:47.580Z
url: https://n8n.io/workflows/5570
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Complete MCP server exposing 2 Analytics API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community

  Import this workflow into your n8n instance
  Credentials Add Analytics API credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the Analytics API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://api.ebay.com{basePath}
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (2 total)

  🔧 Rate_Limit (1 endpoints)

  • GET /rate_limit/: Retrieve Application Rate Limits

  🔧 User_Rate_Limit (1 endpoints)

  • GET /user_rate_limit/: Retrieve User Rate Limits

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native Analytics API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool"]

workflow_json_compressed:
  [encoded data, 4236 characters]

metadata_json:
  {"categories":["AI/ML","Data Processing"],"complexity":"medium","use_cases":["rate limit monitoring","analytics API integration","AI agent data access"],"estimated_setup_minutes":10,"required_services":["eBay Analytics API"],"key_features":["MCP server implementation","AI agent connectivity","Analytics data structuring","Rate limit handling"],"target_audience":["AI developers","data analysts","eBay API integrators"]}


name: eBay Seller Account Management with AI Agent Integration (36 operations)
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-02T03:46:37.835Z
updated_at: 2025-07-02T03:46:37.835Z
url: https://n8n.io/workflows/5571
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:21
description:
  ⚠️ ADVANCED USE ONLY - Account MCP Server (36 operations)

  🚨 This workflow is for advanced users only!

  Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator?

  Join the community

  This MCP server contains 36 operations which is significantly more than the recommended maximum of tools for most AI clients.

  🔍 Recommended Alternative for basic use cases
  Seek a simplified MCP server that utilizes the official n8n tool implementation for Account if available, or an MCP server with only common operations as it will be more efficient and easier to manage.

  🛠️ Advanced Usage Requirements

  BEFORE adding this MCP server to your client:
  Disable or delete unused nodes - Review sections and disable/delete those you don't need

  AFTER adding the MCP server to your client:
  1.Selective tool enabling - Instead of enabling all tools (default), manually select only the specific tools you need for that Workflow's MCP client.
  Monitor performance - Too many tools can slow down AI responses

  💡 Pro Tips
  Keep maximum 40 enabled tools - Most AI clients perform better with fewer tools
  Group related operations and only enable one group at a time
  Use the overview note to understand what each operation group does

  Ping me on discord if your business needs this implemented professionally
  ⚡ Quick Setup

  Import this workflow into your n8n instance
  Credentials Add Account API credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the Account API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://api.ebay.com{basePath}
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (36 total)

  🔧 Advertising_Eligibility (1 endpoints)

  • GET /advertising_eligibility: Check Advertising Eligibility

  🔧 Custom_Policy (4 endpoints)

  • GET /custom_policy/: Update Custom Policy
  • POST /custom_policy/: Create/Search Custom Policy
  • GET /custom_policy/{custom_policy_id}: Get {Custom Policy Id}
  • PUT /custom_policy/{custom_policy_id}: Update {Custom Policy Id}

  🔧 Fulfillment_Policy (6 endpoints)

  • GET /fulfillment_policy: Update Fulfillment Policy
  • POST /fulfillment_policy/: Create/Search Fulfillment Policy
  • GET /fulfillment_policy/get_by_policy_name: This method retrieves the details for a specific fulfillment policy
  • DELETE /fulfillment_policy/{fulfillmentPolicyId}: This method deletes a fulfillment policy
  • GET /fulfillment_policy/{fulfillmentPolicyId}: This method retrieves the complete details of a fulfillment policy
  • PUT /fulfillment_policy/{fulfillmentPolicyId}: This method updates an existing fulfillment policy

  🔧 Kyc (1 endpoints)

  • GET /kyc: Check KYC Status

  🔧 Payment_Policy (6 endpoints)

  • GET /payment_policy: Update Payment Policy
  • POST /payment_policy: Create/Search Payment Policy
  • GET /payment_policy/get_by_policy_name: This method retrieves the details of a specific payment policy
  • DELETE /payment_policy/{payment_policy_id}: This method deletes a payment policy
  • GET /payment_policy/{payment_policy_id}: This method retrieves the complete details of a payment policy
  • PUT /payment_policy/{payment_policy_id}: This method updates an existing payment policy

  🔧 Payments_Program (2 endpoints)

  • GET /payments_program/{marketplace_id}/{payments_program_type}: Get {Payments Program Type}
  • GET /payments_program/{marketplace_id}/{payments_program_type}/onboarding: Get Onboarding

  🔧 Privilege (1 endpoints)

  • GET /privilege: Get Seller Privileges

  🔧 Program (3 endpoints)

  • GET /program/get_opted_in_programs: List Opted-In Programs
  • POST /program/opt_in: Opt In to Program
  • POST /program/opt_out: Opt Out of Program

  🔧 Rate_Table (1 endpoints)

  • GET /rate_table: List Shipping Rate Tables

  🔧 Return_Policy (6 endpoints)

  • GET /return_policy: Update Return Policy
  • POST /return_policy: Create/Search Return Policy
  • GET /return_policy/get_by_policy_name: This method retrieves the details of a specific return policy
  • DELETE /return_policy/{return_policy_id}: This method deletes a return policy
  • GET /return_policy/{return_policy_id}: Get {Return Policy Id}
  • PUT /return_policy/{return_policy_id}: This method updates an existing return policy

  🔧 Sales_Tax (4 endpoints)

  • GET /sales_tax: Update Sales Tax Rate
  • DELETE /sales_tax/{countryCode}/{jurisdictionId}: This call deletes a sales tax table entry for a jurisdiction
  • GET /sales_tax/{countryCode}/{jurisdictionId}: This call gets the current sales tax table entry for a specific tax jurisdiction
  • PUT /sales_tax/{countryCode}/{jurisdictionId}: This method creates or updates a sales tax table entry for a jurisdiction

  🔧 Subscription (1 endpoints)

  • GET /subscription: List Subscriptions

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native Account API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool"]

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metadata_json:
  {"categories":["Data Processing","AI/ML"],"complexity":"complex","use_cases":["centralize eBay seller data streams","orchestrate AI agent responses for account operations","automate retail inventory management workflows"],"estimated_setup_minutes":30,"required_services":["eBay API","AI LLM","MCP Server"],"key_features":["custom MCP server architecture with 36 specialized tools","advanced event-driven data consolidation","intelligent AI agent orchestration for retail tasks"],"target_audience":["enterprise e-commerce managers","advanced n8n developers","AI integration specialists"]}


name: Connect AI Agents to eBay Seller Metrics API via MCP Server
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-02T03:55:13.693Z
updated_at: 2025-07-02T03:55:13.693Z
url: https://n8n.io/workflows/5572
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:21
description:
  Complete MCP server exposing 4  Seller Service Metrics API  API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community

  Import this workflow into your n8n instance
  Credentials Add  Seller Service Metrics API  credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the  Seller Service Metrics API  API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://api.ebay.com{basePath}
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (4 total)

  🔧 Customer_Service_Metric (1 endpoints)

  • GET /customer_service_metric/{customer_service_metric_type}/{evaluation_type}: Get {Evaluation Type}

  🔧 Seller_Standards_Profile (2 endpoints)

  • GET /seller_standards_profile: Get Seller Standards Profile
  • GET /seller_standards_profile/{program}/{cycle}: This call retrieves a single standards profile for the associated seller

  🔧 Traffic_Report (1 endpoints)

  • GET /traffic_report: Retrieve Listing Traffic Report

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native  Seller Service Metrics API  API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool"]

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metadata_json:
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name: Connect AI Agents to eBay Compliance API for Listing Violation Management
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-02T03:57:05.409Z
updated_at: 2025-07-02T03:57:05.409Z
url: https://n8n.io/workflows/5573
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Complete MCP server exposing 3 Compliance API operations to AI agents.

  ⚡ Quick Setup

  Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free?

  Join the community

  Import this workflow into your n8n instance
  Credentials Add Compliance API credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the Compliance API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://api.ebay.com{basePath}
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (3 total)

  🔧 Listing_Violation (1 endpoints)

  • GET /listing_violation: Get Violation Summary Counts

  🔧 Listing_Violation_Summary (1 endpoints)

  • GET /listing_violation_summary: This call returns listing violation counts for a seller

  🔧 Suppress_Listing_Violation (1 endpoints)

  • POST /suppress_listing_violation: Suppress Listing Violation

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native Compliance API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool"]

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metadata_json:
  {"categories":["Data Processing"],"complexity":"medium","use_cases":["monitor listing violations","integrate AI agents with compliance","automate eBay compliance checks"],"estimated_setup_minutes":15,"required_services":["eBay Compliance API","MCP","n8n Webhooks"],"key_features":["MCP server integration","API exposure for AI agents","Webhook triggers"],"target_audience":["eBay sellers","compliance engineers","AI developers"]}


name: AI Agent Integration for eBay Logistics API with MCP Server
author_name: David Ashby
author_username: cfomodz
categories: []
created_at: 2025-07-02T04:00:00.239Z
updated_at: 2025-07-02T04:00:00.239Z
url: https://n8n.io/workflows/5574
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator?

  Join the community

  Complete MCP server exposing 6 Logistics API operations to AI agents.

  ⚡ Quick Setup

  Import this workflow into your n8n instance
  Credentials Add Logistics API credentials
  Activate the workflow to start your MCP server
  Copy the webhook URL from the MCP trigger node
  Connect AI agents using the MCP URL

  🔧 How it Works

  This workflow converts the Logistics API into an MCP-compatible interface for AI agents.

  • MCP Trigger: Serves as your server endpoint for AI agent requests
  • HTTP Request Nodes: Handle API calls to https://api.ebay.com{basePath}
  • AI Expressions: Automatically populate parameters via $fromAI() placeholders
  • Native Integration: Returns responses directly to the AI agent

  📋 Available Operations (6 total)

  🔧 Shipment (4 endpoints)

  • POST /shipment/create_from_shipping_quote: Create Shipment from Quote
  • GET /shipment/{shipmentId}: This method retrieves the shipment details for the specified shipment ID
  • POST /shipment/{shipmentId}/cancel: Create/Search Cancel
  • GET /shipment/{shipmentId}/download_label_file: Get Download Label File

  🔧 Shipping_Quote (2 endpoints)

  • POST /shipping_quote: Retrieve Shipping Quote
  • GET /shipping_quote/{shippingQuoteId}: Get {Shippingquoteid}

  🤖 AI Integration

  Parameter Handling: AI agents automatically provide values for:
  • Path parameters and identifiers
  • Query parameters and filters
  • Request body data
  • Headers and authentication

  Response Format: Native Logistics API responses with full data structure

  Error Handling: Built-in n8n HTTP request error management

  💡 Usage Examples

  Connect this MCP server to any AI agent or workflow:

  • Claude Desktop: Add MCP server URL to configuration
  • Cursor: Add MCP server SSE URL to configuration
  • Custom AI Apps: Use MCP URL as tool endpoint
  • API Integration: Direct HTTP calls to MCP endpoints

  ✨ Benefits

  • Zero Setup: No parameter mapping or configuration needed
  • AI-Ready: Built-in $fromAI() expressions for all parameters
  • Production Ready: Native n8n HTTP request handling and logging
  • Extensible: Easily modify or add custom logic

  &gt; 🆓 Free for community use! Ready to deploy in under 2 minutes.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool"]

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metadata_json:
  {"categories":["Logistics","AI/ML","DevOps"],"complexity":"medium","use_cases":["connect logistics APIs to AI agents","enable MCP server integration for supply chain automation","manage logistics operations via LLM-integrated agents"],"estimated_setup_minutes":10,"required_services":["eBay Logistics API","MCP Server","LLM-compatible AI agent platforms"],"key_features":["exposes 6 logistics API operations to AI agents","webhook-based integration for external AI systems","structured data parsing for agent interactions","secure credentials management for API endpoints"],"target_audience":["supply chain managers","AI engineers","logistics system architects"]}


