LangChain Agent MCP Server

# LangChain Agent MCP Server - Delivery Note **Project:** LangChain Agent MCP Server **Status:** ✅ Complete and Ready for Use **Date:** January 2025 --- ## What You're Receiving A production-ready backend server that exposes LangChain AI agent capabilities through the Model Context Protocol (MCP). The server is fully functional, tested, and ready for deployment. ## Quick Start 1. **Install dependencies:** ```powershell py -m pip install -r requirements.txt ``` 2. **Set your OpenAI API key** in the `.env` file: ``` OPENAI_API_KEY=your-key-here ``` 3. **Start the server:** ```powershell py run_server.py ``` 4. **Access the server:** - API Documentation: http://localhost:8000/docs - Health Check: http://localhost:8000/health - MCP Manifest: http://localhost:8000/mcp/manifest ## Key Features ✅ **MCP-Compliant Endpoints** - Full Model Context Protocol support ✅ **LangChain Agent Integration** - Multi-step reasoning capabilities ✅ **Extensible Tool Framework** - Easy to add custom tools ✅ **Error Handling** - Comprehensive error management ✅ **Docker Support** - Ready for containerized deployment ✅ **Complete Test Suite** - All endpoints tested ✅ **Full Documentation** - Technical docs and client handoff included ## What's Included - Complete source code with documentation - Docker configuration for easy deployment - Comprehensive test suite - Technical documentation (`README_BACKEND.md`) - Client handoff document (`CLIENT_HANDOFF.md`) - Helper scripts for easy startup ## Main Endpoints - **GET `/mcp/manifest`** - Returns available tools - **POST `/mcp/invoke`** - Executes agent with user query ## Configuration All configuration is done via the `.env` file. Key settings: - `OPENAI_API_KEY` (required) - `OPENAI_MODEL` (default: gpt-4o-mini) - `PORT` (default: 8000) - `API_KEY` (optional, for authentication) ## Documentation - **Technical Details:** See `README_BACKEND.md` - **Client Overview:** See `CLIENT_HANDOFF.md` - **API Docs:** Available at `/docs` when server is running ## Support All code is well-documented and follows best practices. For questions: 1. Check the documentation files 2. Review the test suite for usage examples 3. Access the interactive API docs at `/docs` --- **Ready for Production Use** ✅