import { ILlmSchema } from "./ILlmSchema";
import { IValidation } from "./IValidation";

/**
 * MCP LLM function.
 *
 * `IMcpLlmFunction` is an interface representing a function metadata
 * of MCP (Model Context Protocol), which has been designed for the LLM
 * (Language Large Model) function calling, instead of providing
 * [`mcp_servers`](https://openai.github.io/openai-agents-python/mcp/#using-mcp-servers)
 * property to the LLM vendor API.
 *
 * It contains the target function's metadata {@link name}, {@link parameters}
 * and {@link description}. You can put these properties to the LLM vendor API
 * to let the LLM know the function's purpose and how to call it.
 *
 * Also, it contains {@link validate} function, which is used to validate the
 * function arguments generated by AI agent. It is very important to the
 * LLM function calling, because LLM like OpenAI takes a lot of mistakes when
 * composing arguments in function calling. Even though `number` like
 * simple type is defined in the {@link parameters} schema, LLM often
 * fills it just by a `string` typed value.
 *
 * > This is the reason why `@samchon/openapi` recommends not to use the
 * > [`mcp_servers`](https://openai.github.io/openai-agents-python/mcp/#using-mcp-servers)
 * > property of LLM API directly, but to use the function calling feature
 * > instead. You have to make the AI agent to correct the type level mistakes
 * > by using the `validate` function.
 *
 * @author Jeongho Nam - https://github.com/samchon
 * @author Byeongjin Oh - https://github.com/sunrabbit123
 */
export interface IMcpLlmFunction<Model extends ILlmSchema.Model> {
  /**
   * Representative name of the function.
   *
   * @maxLength 64
   */
  name: string;

  /**
   * Description of the function.
   *
   * For reference, the `description` is very important property to teach
   * the purpose of the function to the LLM (Language Large Model), and
   * LLM actually determines which function to call by the description.
   *
   * Also, when the LLM conversates with the user, the `description` is
   * used to explain the function to the user. Therefore, the `description`
   * property has the highest priority, and you have to consider it.
   */
  description?: string | undefined;

  /**
   * Parameters of the function.
   */
  parameters: ILlmSchema.IParameters<Model>;

  /**
   * Validate function of the arguments.
   *
   * You know what? LLM (Large Language Model) like OpenAI takes a lot of
   * mistakes when composing arguments in function calling. Even though
   * `number` like simple type is defined in the {@link parameters} schema,
   * LLM often fills it just by a `string` typed value.
   *
   * In that case, you have to give a validation feedback to the LLM by
   * using this `validate` function. The `validate` function will return
   * detailed information about every type errors about the arguments.
   *
   * And in my experience, OpenAI's `gpt-4o-mini` model tends to construct
   * an invalid function calling arguments at the first trial about 50% of
   * the time. However, if correct it through this `validate` function,
   * the success rate soars to 99% at the second trial, and I've never
   * failed at the third trial.
   *
   * > If you've {@link separated} parameters, use the
   * > {@link IMcpLlmFunction.ISeparated.validate} function instead when
   * > validating the LLM composed arguments.
   * >
   * > In that case, This `validate` function would be meaningful only
   * > when you've merged the LLM and human composed arguments by
   * > {@link McpLlm.mergeParameters} function.
   *
   * @param args Arguments to validate
   * @returns Validation result
   */
  validate: (args: unknown) => IValidation<unknown>;
}