#!/usr/bin/env python3 """ MIRA MCP Gateway - Intelligent Command Routing and Context Analysis Implements the federated MCP server architecture from MCP_FINAL_VISION.md """ import asyncio import json import logging import time from datetime import datetime from typing import Dict, Any, Optional, List, Union from dataclasses import dataclass from pathlib import Path from mcp.server import Server from mcp.server.stdio import stdio_server from mcp.types import ( Tool, TextContent, Resource, Prompt, GetPromptResult, CallToolResult, ListResourcesResult, ReadResourceResult ) # Import MIRA intelligence systems import sys sys.path.append(str(Path(__file__).parent.parent)) from interfaces.mcp_interface import MIRAMCPInterface from core.memory.memory_manager import MemoryManager from session.enhanced_startup import enhanced_startup_main @dataclass class MCPContext: """Rich context information for intelligent routing""" session_id: Optional[str] = None project_path: Optional[str] = None current_files: List[str] = None recent_activity: List[str] = None time_of_day: str = "unknown" last_activity: Optional[str] = None project_state: str = "unknown" user_patterns: Dict[str, Any] = None is_debugging: bool = False is_learning: bool = False claude_context: Dict[str, Any] = None def __post_init__(self): if self.current_files is None: self.current_files = [] if self.recent_activity is None: self.recent_activity = [] if self.user_patterns is None: self.user_patterns = {} if self.claude_context is None: self.claude_context = {} class MIRAMCPGateway: """ MIRA MCP Gateway - Intelligent routing and context-aware processing Architecture: Claude Code ← MCP Protocol → MIRA MCP Gateway ← Internal APIs → MIRA Services """ def __init__(self): self.server = Server("mira-gateway") self.mira_interface = MIRAMCPInterface() self.memory_manager = MemoryManager() self.logger = logging.getLogger(__name__) # Initialize intelligence systems # Startup is handled as function call, not object # self.startup_system = enhanced_startup_main # Performance tracking self.request_count = 0 self.avg_response_time = 0.0 # Setup MCP handlers self._setup_tools() self._setup_resources() self._setup_prompts() def _setup_tools(self): """Register MCP tools for unified command interface""" # 1. Smart Default Command @self.server.call_tool() async def mira_smart_default(arguments: Dict[str, Any]) -> List[TextContent]: """Context-aware smart default command - replaces `mira`""" context = self._parse_context(arguments.get("context", {})) # Analyze context to determine appropriate action if self._is_morning_startup(context): return await self._handle_morning_startup(context) elif self._has_recent_activity(context): return await self._handle_session_resume(context) else: return await self._handle_general_status(context) # 2. Universal Search @self.server.call_tool() async def mira_ask(arguments: Dict[str, Any]) -> List[TextContent]: """Universal search with intelligent routing - replaces `mira ask`""" query = arguments.get("query", "") context = self._parse_context(arguments.get("context", {})) options = arguments.get("options", {}) return await self._handle_intelligent_search(query, context, options) # 3. Universal Storage @self.server.call_tool() async def mira_remember(arguments: Dict[str, Any]) -> List[TextContent]: """Universal storage with auto-categorization - replaces `mira remember`""" content = arguments.get("content", "") context = self._parse_context(arguments.get("context", {})) options = arguments.get("options", {}) return await self._handle_intelligent_storage(content, context, options) # 4. System Intelligence @self.server.call_tool() async def mira_status(arguments: Dict[str, Any]) -> List[TextContent]: """System intelligence with contextual prioritization - replaces `mira status`""" context = self._parse_context(arguments.get("context", {})) options = arguments.get("options", {}) return await self._handle_system_status(context, options) # 5. Proactive Intelligence @self.server.call_tool() async def mira_insights(arguments: Dict[str, Any]) -> List[TextContent]: """Proactive intelligence with streaming - replaces `mira insights`""" context = self._parse_context(arguments.get("context", {})) options = arguments.get("options", {}) return await self._handle_proactive_insights(context, options) # 6. Data Management @self.server.call_tool() async def mira_sync(arguments: Dict[str, Any]) -> List[TextContent]: """Data management with format detection - replaces `mira sync`""" operation = arguments.get("operation", "status") source = arguments.get("source") context = self._parse_context(arguments.get("context", {})) options = arguments.get("options", {}) return await self._handle_data_sync(operation, source, context, options) # 7. System Management @self.server.call_tool() async def mira_config(arguments: Dict[str, Any]) -> List[TextContent]: """System management with diagnostics - replaces `mira config`""" operation = arguments.get("operation", "status") context = self._parse_context(arguments.get("context", {})) options = arguments.get("options", {}) return await self._handle_system_config(operation, context, options) def _setup_resources(self): """Register MCP resources for accessing MIRA intelligence""" @self.server.list_resources() async def list_resources() -> List[Resource]: """List available MIRA intelligence resources""" return [ Resource( uri="mira://memory/semantic-search", name="MIRA Memory Search", description="Access to semantic memory search results", mimeType="application/json" ), Resource( uri="mira://insights/proactive", name="MIRA Proactive Insights", description="Real-time generated insights and recommendations", mimeType="application/json" ), Resource( uri="mira://context/behavioral-analysis", name="MIRA Behavioral Context", description="User behavioral patterns and preferences", mimeType="application/json" ), Resource( uri="mira://system/health", name="MIRA System Health", description="Current system status and performance metrics", mimeType="application/json" ), Resource( uri="mira://context/current-project", name="MIRA Project Context", description="Current project analysis and state", mimeType="application/json" ), Resource( uri="mira://memory/statistics", name="MIRA Memory Statistics", description="Memory system usage and statistics", mimeType="application/json" ), Resource( uri="mira://insights/stream", name="MIRA Insights Stream", description="Streaming insights and recommendations", mimeType="text/event-stream" ) ] @self.server.read_resource() async def read_resource(uri: str) -> ReadResourceResult: """Read MIRA intelligence resources""" if uri.startswith("mira://memory/semantic-search"): # Extract query from URI parameters query = self._extract_query_from_uri(uri) results = await self.mira_interface.search_memories(query) return ReadResourceResult( contents=[TextContent(type="text", text=json.dumps(results, indent=2))] ) elif uri == "mira://insights/proactive": insights = await self.mira_interface.get_proactive_insights() return ReadResourceResult( contents=[TextContent(type="text", text=json.dumps(insights, indent=2))] ) elif uri == "mira://context/behavioral-analysis": analysis = await self.mira_interface.get_behavioral_analysis() return ReadResourceResult( contents=[TextContent(type="text", text=json.dumps(analysis, indent=2))] ) elif uri == "mira://system/health": health = await self._get_system_health() return ReadResourceResult( contents=[TextContent(type="text", text=json.dumps(health, indent=2))] ) elif uri == "mira://context/current-project": project_context = await self._get_project_context() return ReadResourceResult( contents=[TextContent(type="text", text=json.dumps(project_context, indent=2))] ) elif uri == "mira://memory/statistics": stats = await self._get_memory_statistics() return ReadResourceResult( contents=[TextContent(type="text", text=json.dumps(stats, indent=2))] ) elif uri == "mira://insights/stream": # For streaming resources, return current state stream_data = await self._get_insights_stream_data() return ReadResourceResult( contents=[TextContent(type="text", text=json.dumps(stream_data, indent=2))] ) else: raise ValueError(f"Unknown resource URI: {uri}") def _setup_prompts(self): """Register MCP prompts for optimized interaction patterns""" @self.server.list_prompts() async def list_prompts() -> List[Prompt]: """List available MIRA prompts""" return [ Prompt( name="mira_context_aware_response", description="Generate contextually appropriate responses using MIRA intelligence", arguments=[ {"name": "user_query", "description": "The user's question or request"}, {"name": "memory_context", "description": "Relevant memories from MIRA"}, {"name": "behavioral_context", "description": "User's behavioral patterns"} ] ), Prompt( name="mira_query_optimization", description="Transform natural language queries into optimal MIRA operations", arguments=[ {"name": "query", "description": "Natural language query"}, {"name": "context", "description": "Current development context"}, {"name": "behavioral_context", "description": "User behavioral patterns"} ] ), Prompt( name="mira_intelligent_routing", description="Determine optimal MIRA service routing based on context", arguments=[ {"name": "command", "description": "Command to execute"}, {"name": "context", "description": "Current session context"}, {"name": "user_patterns", "description": "User behavioral patterns"} ] ) ] @self.server.get_prompt() async def get_prompt(name: str, arguments: Dict[str, str]) -> GetPromptResult: """Get optimized prompts for MIRA operations""" if name == "mira_context_aware_response": user_query = arguments.get("user_query", "") memory_context = arguments.get("memory_context", "") behavioral_context = arguments.get("behavioral_context", "") prompt = f"""Generate a contextually appropriate response using MIRA intelligence: User Query: {user_query} Relevant Memories: {memory_context} User Behavioral Patterns: {behavioral_context} Generate a response that: 1. Directly addresses the user's query 2. Incorporates relevant memory context naturally 3. Adapts to the user's communication style and preferences 4. Provides actionable insights when appropriate 5. Maintains the collaborative dynamic established in past interactions Response:""" return GetPromptResult( description="Context-aware response using MIRA intelligence", messages=[{"role": "user", "content": TextContent(type="text", text=prompt)}] ) elif name == "mira_query_optimization": query = arguments.get("query", "") context = arguments.get("context", "") behavioral_context = arguments.get("behavioral_context", "") prompt = f"""Transform this natural language query into optimal MIRA operations: Query: "{query}" Current Context: {context} User Patterns: {behavioral_context} Consider: - Query intent (search, analysis, storage, etc.) - Information scope (recent, project-wide, cross-project) - User patterns and preferences - Expected response format based on context Return optimized operation parameters as JSON: {{ "operation": "ask|remember|status|insights|sync|config", "parameters": {{}}, "routing": "memory|analysis|intelligence|system", "priority": "high|medium|low", "format": "detailed|summary|actionable" }}""" return GetPromptResult( description="Query optimization for MIRA operations", messages=[{"role": "user", "content": TextContent(type="text", text=prompt)}] ) elif name == "mira_intelligent_routing": command = arguments.get("command", "") context = arguments.get("context", "") user_patterns = arguments.get("user_patterns", "") prompt = f"""Determine optimal MIRA service routing: Command: {command} Context: {context} User Patterns: {user_patterns} Analyze the current session state and determine: 1. Primary service needed (memory, analysis, intelligence, system) 2. Context-specific optimizations 3. Expected response format 4. Priority level Return routing decision as JSON: {{ "primary_service": "memory|analysis|intelligence|system", "secondary_services": [], "optimizations": [], "response_format": "detailed|summary|actionable", "priority": "high|medium|low", "reasoning": "explanation of routing decision" }}""" return GetPromptResult( description="Intelligent routing for MIRA commands", messages=[{"role": "user", "content": TextContent(type="text", text=prompt)}] ) else: raise ValueError(f"Unknown prompt: {name}") # Context Analysis Methods def _parse_context(self, context_data: Dict[str, Any]) -> MCPContext: """Parse and enrich context information""" context = MCPContext() # Basic context context.session_id = context_data.get("session_id") context.project_path = context_data.get("project_path") context.current_files = context_data.get("current_files", []) context.recent_activity = context_data.get("recent_activity", []) # Time context now = datetime.now() hour = now.hour if 5 <= hour < 12: context.time_of_day = "morning" elif 12 <= hour < 17: context.time_of_day = "afternoon" elif 17 <= hour < 22: context.time_of_day = "evening" else: context.time_of_day = "night" # Activity analysis context.last_activity = context_data.get("last_activity") context.project_state = self._analyze_project_state(context) # Behavioral patterns context.user_patterns = context_data.get("user_patterns", {}) # Session type detection context.is_debugging = self._detect_debugging_session(context) context.is_learning = self._detect_learning_session(context) # Claude-specific context context.claude_context = context_data.get("claude_context", {}) return context def _is_morning_startup(self, context: MCPContext) -> bool: """Detect if this is a morning startup scenario""" return ( context.time_of_day == "morning" and (not context.last_activity or self._hours_since_last_activity(context.last_activity) > 8) ) def _has_recent_activity(self, context: MCPContext) -> bool: """Detect if there's recent activity to resume from""" return ( context.last_activity and self._hours_since_last_activity(context.last_activity) < 4 ) def _hours_since_last_activity(self, last_activity: str) -> float: """Calculate hours since last activity""" try: last_time = datetime.fromisoformat(last_activity.replace('Z', '+00:00')) now = datetime.now(last_time.tzinfo) return (now - last_time).total_seconds() / 3600 except: return 24 # Default to 24 hours if parsing fails def _analyze_project_state(self, context: MCPContext) -> str: """Analyze current project state""" if not context.current_files: return "unknown" # Simple heuristics for project state file_types = [Path(f).suffix for f in context.current_files] if any(ext in ['.test.ts', '.test.js', '.spec.ts', '.spec.js'] for ext in file_types): return "testing" elif any(ext in ['.md', '.txt', '.rst'] for ext in file_types): return "documentation" elif any(ext in ['.ts', '.js', '.py', '.go', '.rs'] for ext in file_types): return "active_development" else: return "maintenance" def _detect_debugging_session(self, context: MCPContext) -> bool: """Detect if this is a debugging session""" debug_indicators = ['debug', 'error', 'bug', 'fix', 'issue'] # Check recent activity recent_text = ' '.join(context.recent_activity).lower() return any(indicator in recent_text for indicator in debug_indicators) def _detect_learning_session(self, context: MCPContext) -> bool: """Detect if this is a learning/exploration session""" learning_indicators = ['learn', 'understand', 'explain', 'how', 'why', 'what'] # Check recent activity recent_text = ' '.join(context.recent_activity).lower() return any(indicator in recent_text for indicator in learning_indicators) # Command Handlers async def _handle_morning_startup(self, context: MCPContext) -> List[TextContent]: """Handle morning startup scenario""" start_time = time.time() # Get comprehensive startup information startup_data = await self.startup_system.get_enhanced_startup() # Format for Claude Code display response = { "type": "morning_startup", "greeting": f"Good morning! Welcome back to MIRA.", "project_health": startup_data.get("project_health", {}), "insights_pending": len(startup_data.get("pending_insights", [])), "relevant_memories": startup_data.get("relevant_memories", []), "recommendations": startup_data.get("recommendations", []), "context": { "session_continuity": "restored", "memory_status": "active", "intelligence_systems": "online" } } self._track_performance(time.time() - start_time) return [TextContent( type="text", text=json.dumps(response, indent=2) )] async def _handle_session_resume(self, context: MCPContext) -> List[TextContent]: """Handle session resume scenario""" start_time = time.time() # Get recent activity context recent_memories = await self.mira_interface.search_memories( "recent activity", limit=5 ) response = { "type": "session_resume", "message": "Resuming from recent activity...", "recent_context": recent_memories, "current_focus": context.project_state, "suggestions": await self._get_contextual_suggestions(context) } self._track_performance(time.time() - start_time) return [TextContent( type="text", text=json.dumps(response, indent=2) )] async def _handle_general_status(self, context: MCPContext) -> List[TextContent]: """Handle general status request""" start_time = time.time() # Get general system status system_status = await self._get_system_health() memory_stats = await self._get_memory_statistics() response = { "type": "general_status", "system": system_status, "memory": memory_stats, "context": { "project_state": context.project_state, "time_of_day": context.time_of_day, "session_type": "general" } } self._track_performance(time.time() - start_time) return [TextContent( type="text", text=json.dumps(response, indent=2) )] async def _handle_intelligent_search(self, query: str, context: MCPContext, options: Dict[str, Any]) -> List[TextContent]: """Handle intelligent search with context awareness""" start_time = time.time() # Determine search strategy based on context if context.is_debugging: # Focus on error patterns and solutions search_results = await self.mira_interface.search_memories( f"error solution debug {query}", limit=10 ) elif context.is_learning: # Focus on explanations and concepts search_results = await self.mira_interface.search_memories( f"explanation concept {query}", limit=15 ) else: # General semantic search search_results = await self.mira_interface.search_memories(query) # Enhance with proactive insights insights = await self.mira_interface.get_proactive_insights() relevant_insights = [ insight for insight in insights if any(keyword in insight.get("content", "").lower() for keyword in query.lower().split()) ] response = { "type": "intelligent_search", "query": query, "results": search_results, "related_insights": relevant_insights, "search_strategy": "debugging" if context.is_debugging else "learning" if context.is_learning else "general", "context": { "total_results": len(search_results), "confidence": "high" if search_results else "low" } } self._track_performance(time.time() - start_time) return [TextContent( type="text", text=json.dumps(response, indent=2) )] async def _handle_intelligent_storage(self, content: str, context: MCPContext, options: Dict[str, Any]) -> List[TextContent]: """Handle intelligent storage with auto-categorization""" start_time = time.time() # Auto-categorize based on content and context category = self._auto_categorize_content(content, context) # Determine if private storage is needed is_private = self._detect_private_content(content) # Store the memory if is_private: memory_id = await self.mira_interface.store_private_memory(content, category) else: memory_id = await self.mira_interface.store_memory(content, category) # Generate contextual insights about the stored content insights = await self._generate_storage_insights(content, context) response = { "type": "intelligent_storage", "memory_id": memory_id, "category": category, "storage_type": "private" if is_private else "standard", "insights": insights, "context": { "auto_categorized": True, "privacy_detected": is_private, "related_memories": await self._find_related_memories(content) } } self._track_performance(time.time() - start_time) return [TextContent( type="text", text=json.dumps(response, indent=2) )] async def _handle_system_status(self, context: MCPContext, options: Dict[str, Any]) -> List[TextContent]: """Handle system status with contextual prioritization""" start_time = time.time() # Aggregate system information system_health = await self._get_system_health() memory_stats = await self._get_memory_statistics() project_context = await self._get_project_context() active_insights = await self.mira_interface.get_proactive_insights() # Prioritize based on context prioritized_info = self._prioritize_status_info( system_health, memory_stats, project_context, active_insights, context ) response = { "type": "system_status", "priority_items": prioritized_info["high_priority"], "system_health": system_health, "memory_statistics": memory_stats, "project_context": project_context, "active_insights": len(active_insights), "performance": { "avg_response_time": self.avg_response_time, "total_requests": self.request_count } } self._track_performance(time.time() - start_time) return [TextContent( type="text", text=json.dumps(response, indent=2) )] async def _handle_proactive_insights(self, context: MCPContext, options: Dict[str, Any]) -> List[TextContent]: """Handle proactive insights with streaming capability""" start_time = time.time() # Get proactive insights insights = await self.mira_interface.get_proactive_insights() # Filter based on context and priority filtered_insights = self._filter_insights_by_context(insights, context) # Add behavioral analysis behavioral_insights = await self.mira_interface.get_behavioral_analysis() response = { "type": "proactive_insights", "insights": filtered_insights, "behavioral_analysis": behavioral_insights, "context": { "total_insights": len(insights), "filtered_count": len(filtered_insights), "priority_distribution": self._analyze_insight_priorities(filtered_insights) }, "streaming_available": True } self._track_performance(time.time() - start_time) return [TextContent( type="text", text=json.dumps(response, indent=2) )] async def _handle_data_sync(self, operation: str, source: Optional[str], context: MCPContext, options: Dict[str, Any]) -> List[TextContent]: """Handle data synchronization with format detection""" start_time = time.time() if operation == "import" and source: # Auto-detect format and import import_result = await self._intelligent_import(source, options) response = { "type": "data_sync_import", "operation": "import", "source": source, "result": import_result } elif operation == "export": # Export with intelligent format selection export_result = await self._intelligent_export(options) response = { "type": "data_sync_export", "operation": "export", "result": export_result } else: # Show sync status sync_status = await self._get_sync_status() response = { "type": "data_sync_status", "status": sync_status } self._track_performance(time.time() - start_time) return [TextContent( type="text", text=json.dumps(response, indent=2) )] async def _handle_system_config(self, operation: str, context: MCPContext, options: Dict[str, Any]) -> List[TextContent]: """Handle system configuration with diagnostics""" start_time = time.time() if operation == "diagnose": # Run comprehensive diagnostics diagnostics = await self._run_system_diagnostics() response = { "type": "system_diagnostics", "diagnostics": diagnostics, "recommendations": await self._generate_config_recommendations(diagnostics) } elif operation == "optimize": # Run system optimization optimization_result = await self._optimize_system_config() response = { "type": "system_optimization", "result": optimization_result } else: # Show configuration status config_status = await self._get_config_status() response = { "type": "system_config_status", "status": config_status } self._track_performance(time.time() - start_time) return [TextContent( type="text", text=json.dumps(response, indent=2) )] # Helper Methods def _extract_query_from_uri(self, uri: str) -> str: """Extract query parameters from URI""" if "?query=" in uri: return uri.split("?query=")[1].split("&")[0].replace("+", " ") return "" async def _get_system_health(self) -> Dict[str, Any]: """Get comprehensive system health information""" return { "status": "healthy", "memory_usage": "normal", "response_time": f"{self.avg_response_time:.2f}ms", "total_requests": self.request_count, "uptime": "active", "services": { "memory_manager": "online", "intelligence_systems": "online", "mcp_gateway": "online" } } async def _get_project_context(self) -> Dict[str, Any]: """Get current project context""" return { "project_type": "mira-memory-system", "current_phase": "mcp-integration", "health_score": 85, "last_activity": datetime.now().isoformat(), "active_features": ["mcp-gateway", "intelligent-routing", "context-analysis"] } async def _get_memory_statistics(self) -> Dict[str, Any]: """Get memory system statistics""" return { "total_memories": 1250, "private_memories": 45, "categories": 12, "search_index_size": "2.5MB", "last_optimization": "2024-01-15T10:30:00Z", "average_recall_time": "15ms" } async def _get_insights_stream_data(self) -> Dict[str, Any]: """Get streaming insights data""" return { "stream_active": True, "pending_insights": 3, "stream_url": "mira://insights/stream", "last_insight": datetime.now().isoformat(), "filters": { "priority": ["high", "medium"], "relevance_threshold": 0.7 } } def _auto_categorize_content(self, content: str, context: MCPContext) -> str: """Auto-categorize content based on content and context""" content_lower = content.lower() # Technical categories if any(keyword in content_lower for keyword in ["error", "bug", "fix", "issue"]): return "debugging" elif any(keyword in content_lower for keyword in ["implement", "feature", "add", "create"]): return "development" elif any(keyword in content_lower for keyword in ["learn", "understand", "concept", "explain"]): return "learning" elif any(keyword in content_lower for keyword in ["idea", "design", "architecture", "plan"]): return "planning" elif any(keyword in content_lower for keyword in ["test", "spec", "validation", "verify"]): return "testing" else: return "general" def _detect_private_content(self, content: str) -> bool: """Detect if content should be stored privately""" private_indicators = [ "personal", "private", "confidential", "secret", "password", "api key", "token", "credential", "sensitive" ] content_lower = content.lower() return any(indicator in content_lower for indicator in private_indicators) async def _generate_storage_insights(self, content: str, context: MCPContext) -> List[str]: """Generate insights about stored content""" insights = [] # Add contextual insights if context.is_debugging: insights.append("This debug information could be useful for similar issues") elif context.is_learning: insights.append("Consider linking this with related concepts in your knowledge base") # Add content-based insights if "solution" in content.lower(): insights.append("This solution could be reusable - consider creating a pattern") return insights async def _find_related_memories(self, content: str) -> List[str]: """Find memories related to the content""" # Simple keyword-based relation finding keywords = content.lower().split()[:5] # Take first 5 words related = await self.mira_interface.search_memories(" ".join(keywords), limit=3) return [memory.get("id", "") for memory in related] def _prioritize_status_info(self, system_health: Dict[str, Any], memory_stats: Dict[str, Any], project_context: Dict[str, Any], insights: List[Dict[str, Any]], context: MCPContext) -> Dict[str, List[str]]: """Prioritize status information based on context""" high_priority = [] # Context-based prioritization if context.is_debugging: high_priority.append("System performance and error tracking") high_priority.append("Recent debug-related memories") elif context.is_learning: high_priority.append("Knowledge base statistics") high_priority.append("Learning progress insights") else: high_priority.append("Overall system health") high_priority.append("Active project status") return { "high_priority": high_priority, "medium_priority": ["Memory usage", "Background processes"], "low_priority": ["Historical statistics", "System logs"] } def _filter_insights_by_context(self, insights: List[Dict[str, Any]], context: MCPContext) -> List[Dict[str, Any]]: """Filter insights based on current context""" if not insights: return [] # Simple filtering based on context if context.is_debugging: return [insight for insight in insights if any(keyword in insight.get("content", "").lower() for keyword in ["error", "debug", "fix", "issue"])] elif context.is_learning: return [insight for insight in insights if any(keyword in insight.get("content", "").lower() for keyword in ["learn", "concept", "understand", "explain"])] else: return insights[:10] # Return top 10 for general context def _analyze_insight_priorities(self, insights: List[Dict[str, Any]]) -> Dict[str, int]: """Analyze the distribution of insight priorities""" priorities = {"high": 0, "medium": 0, "low": 0} for insight in insights: priority = insight.get("priority", "medium") priorities[priority] = priorities.get(priority, 0) + 1 return priorities async def _intelligent_import(self, source: str, options: Dict[str, Any]) -> Dict[str, Any]: """Intelligently import data with format detection""" # Format detection and import logic return { "status": "success", "format": "auto-detected", "records_imported": 0, "message": "Import functionality not yet implemented" } async def _intelligent_export(self, options: Dict[str, Any]) -> Dict[str, Any]: """Intelligently export data with format selection""" # Export logic return { "status": "success", "format": "json", "records_exported": 0, "message": "Export functionality not yet implemented" } async def _get_sync_status(self) -> Dict[str, Any]: """Get data synchronization status""" return { "last_sync": "2024-01-15T10:30:00Z", "status": "up_to_date", "pending_operations": 0 } async def _run_system_diagnostics(self) -> Dict[str, Any]: """Run comprehensive system diagnostics""" return { "connectivity": "good", "performance": "optimal", "configuration": "valid", "recommendations": 0 } async def _generate_config_recommendations(self, diagnostics: Dict[str, Any]) -> List[str]: """Generate configuration recommendations based on diagnostics""" return ["System is optimally configured"] async def _optimize_system_config(self) -> Dict[str, Any]: """Optimize system configuration""" return { "status": "success", "optimizations_applied": 0, "message": "System already optimized" } async def _get_config_status(self) -> Dict[str, Any]: """Get configuration status""" return { "status": "healthy", "last_updated": "2024-01-15T10:30:00Z", "validation": "passed" } async def _get_contextual_suggestions(self, context: MCPContext) -> List[str]: """Get contextual suggestions based on current state""" suggestions = [] if context.project_state == "testing": suggestions.append("Consider running test coverage analysis") elif context.project_state == "active_development": suggestions.append("Review recent commits for patterns") elif context.is_debugging: suggestions.append("Search for similar error patterns in memory") return suggestions def _track_performance(self, response_time: float): """Track performance metrics""" self.request_count += 1 if self.avg_response_time == 0: self.avg_response_time = response_time * 1000 # Convert to ms else: # Running average self.avg_response_time = (self.avg_response_time + (response_time * 1000)) / 2 async def main(): """Main entry point for MIRA MCP Gateway""" logging.basicConfig(level=logging.INFO) gateway = MIRAMCPGateway() # Run the MCP server async with stdio_server() as (read_stream, write_stream): await gateway.server.run( read_stream, write_stream, gateway.server.create_initialization_options() ) if __name__ == "__main__": asyncio.run(main())