mcp-client

# Simple MCP Client with LangGraph Agent A lightweight implementation of a multiserver MCP (Model Context Protocol) client using a LangGraph agent with Express.js and TypeScript. ## Features - Express.js server with TypeScript - LangGraph agent for handling conversations - MCP client for tool integration - Streaming responses ## Setup 1. Clone the repository 2. Install dependencies: `npm install` 3. Create a `.env` file with the following variables: ``` PORT=3000 NODE_ENV=development CORS_ORIGIN=* ANTHROPIC_API_KEY=your_anthropic_api_key ``` 4. Build and start the server: `npm run buildnpm start` ## API Endpoints ### Initialize Chat ``` GET /api/chat/init ``` ### Generate Chat ``` POST /api/chat ``` Request body: ```json { "input": "Hello, how can you help me?", "characterId": "character_123", "llmConfig": { "model": "claude-3-opus-20240229", "verbose": true }, "chatHistory": [], "mcpServers": [ { "name": "weather-service", "version": "1.0", "url": "https://example-mcp-server.com", "key": "optional_api_key" } ] } ``` ### Close Chat ``` GET /api/chat/close ``` ## MCP Integration The server connects to MCP servers specified in the request and retrieves available tools that can be used by the LangGraph agent. ## Development Run the development server with auto-reload: ``` npm run dev ``` ## Example Usage ```typescript // Send a message and stream the response const response = await fetch("/api/chat", { method: "POST", headers: { "Content-Type": "application/json" }, body: JSON.stringify({ input: "What's the weather in New York?", characterId: "agent1", chatHistory: [], mcpServers: [ { name: "weather-service", version: "1.0", url: "https://my-mcp-server.com", }, ], }), }); // Read the streaming response const reader = response.body.getReader(); while (true) { const { done, value } = await reader.read(); if (done) break; const text = new TextDecoder().decode(value); const events = text.split("\n\n").filter(Boolean); for (const event of events) { const data = JSON.parse(event); console.log(data); } } ```