@smartsamurai/krapi-sdk/src/mcp-service.ts

KRAPI TypeScript SDK - Easy-to-use client SDK for connecting to self-hosted KRAPI servers (like Appwrite SDK)

/**
 * MCP Service for BackendSDK
 * 
 * Provides Model Context Protocol (MCP) functionality for AI model interactions.
 * Supports chat operations, model capabilities, and model management.
 * 
 * @module mcp-service
 */

import { DatabaseConnection, Logger } from "./core";
import { normalizeError } from "./utils/error-handler";

export interface ChatMessage {
  role: "user" | "assistant" | "system";
  content: string;
  timestamp?: string;
}

export interface ChatResponse {
  message: string;
  model: string;
  usage?: {
    prompt_tokens: number;
    completion_tokens: number;
    total_tokens: number;
  };
  finish_reason?: string;
}

export interface ModelCapabilities {
  models: string[];
  features: string[];
  max_tokens: number;
  supports_streaming: boolean;
  supports_functions: boolean;
}

export interface Model {
  id: string;
  name: string;
  provider: string;
  capabilities: string[];
  max_tokens: number;
  is_available: boolean;
}

/**
 * MCP Service for BackendSDK
 * 
 * Provides MCP functionality for AI model interactions.
 * 
 * @class MCPService
 * @example
 * const mcpService = new MCPService(dbConnection, logger);
 * const response = await mcpService.chat('project-id', [{ role: 'user', content: 'Hello' }]);
 */
export class MCPService {
  constructor(
     
    // @ts-expect-error - Reserved for future use
    private _dbConnection: DatabaseConnection,
    private logger: Logger = console
  ) {}

  /**
   * Get model capabilities
   * 
   * @returns {Promise<ModelCapabilities>} Model capabilities
   */
  async getModelCapabilities(): Promise<ModelCapabilities> {
    // Default capabilities - in a real implementation, this would query the MCP server
    return {
      models: ["gpt-4", "gpt-3.5-turbo", "claude-3-opus"],
      features: ["chat", "completion", "streaming"],
      max_tokens: 4096,
      supports_streaming: true,
      supports_functions: true,
    };
  }

  /**
   * Send chat messages to MCP model
   * 
   * @param {string} projectId - Project ID
   * @param {ChatMessage[]} messages - Chat messages
   * @returns {Promise<ChatResponse>} Chat response
   */
  async chat(projectId: string, messages: ChatMessage[]): Promise<ChatResponse> {
    try {
      // In a real implementation, this would call the MCP server
      // For now, return a mock response
      this.logger.info(`MCP chat for project ${projectId} with ${messages.length} messages`);
      
      return {
        message: "This is a mock MCP response. MCP integration needs to be implemented.",
        model: "gpt-4",
        usage: {
          prompt_tokens: 100,
          completion_tokens: 50,
          total_tokens: 150,
        },
        finish_reason: "stop",
      };
      } catch (error) {
        this.logger.error("Failed to send MCP chat:", error);
        throw normalizeError(error, "INTERNAL_ERROR", {
          operation: "chat",
          projectId
        });
      }
  }

  /**
   * Send chat messages to MCP model (admin context)
   * 
   * @param {ChatMessage[]} messages - Chat messages
   * @returns {Promise<ChatResponse>} Chat response
   */
  async adminChat(messages: ChatMessage[]): Promise<ChatResponse> {
    try {
      // In a real implementation, this would call the MCP server with admin context
      this.logger.info(`MCP admin chat with ${messages.length} messages`);
      
      return {
        message: "This is a mock MCP admin response. MCP integration needs to be implemented.",
        model: "gpt-4",
        usage: {
          prompt_tokens: 100,
          completion_tokens: 50,
          total_tokens: 150,
        },
        finish_reason: "stop",
      };
      } catch (error) {
        this.logger.error("Failed to send MCP admin chat:", error);
        throw normalizeError(error, "INTERNAL_ERROR", {
          operation: "adminChat"
        });
      }
  }

  /**
   * List available MCP models
   * 
   * @returns {Promise<Model[]>} List of available models
   */
  async listModels(): Promise<Model[]> {
    // In a real implementation, this would query the MCP server
    return [
      {
        id: "gpt-4",
        name: "GPT-4",
        provider: "openai",
        capabilities: ["chat", "completion", "streaming"],
        max_tokens: 8192,
        is_available: true,
      },
      {
        id: "gpt-3.5-turbo",
        name: "GPT-3.5 Turbo",
        provider: "openai",
        capabilities: ["chat", "completion", "streaming"],
        max_tokens: 4096,
        is_available: true,
      },
      {
        id: "claude-3-opus",
        name: "Claude 3 Opus",
        provider: "anthropic",
        capabilities: ["chat", "completion"],
        max_tokens: 200000,
        is_available: true,
      },
    ];
  }
}