Model Context Protocol Multi-Agent Server

# Model Context Protocol (MCP) Multi-Agent Demo This project demonstrates how to set up and communicate with custom Model Context Protocol (MCP) servers in Python. It showcases multi-agent orchestration using LangChain, Groq, and MCP adapters, enabling both local and remote tool integration. ## Features - **Custom MCP Servers:** Math and Weather agents, each as independent MCP servers - **Multi-Transport Communication:** Local (stdio) and remote (HTTP) transports - **LangChain Integration:** Unified agent interface for tool invocation - **Async Orchestration:** Efficient, non-blocking agent communication ## Components ### 1. `mathserver.py` A custom MCP server providing math operations (add, multiply) via stdio transport. ### 2. `weather.py` A custom MCP server providing weather information via HTTP transport - (Static content for demo). ### 3. `client.py` A Python client that connects to both servers, discovers their tools, and invokes them using a LangChain agent powered by Groq. ## Setup Instructions 1. **Clone the repository** 2. **Install dependencies:** ```bash pip install -r requirements.txt ``` 3. **Set up environment variables:** - Create a `.env` file with your `GROQ_API_KEY`: ```env GROQ_API_KEY=your_groq_api_key_here ``` 4. **Run the servers:** - Start the weather server (in one terminal): ```bash python weather.py ``` - The math server is started automatically by the client when needed. 5. **Run the client:** ```bash python client.py ``` ## Example Output ``` Math Response: The answer is 900 Weather Response: The weather in delhi is sunny ``` ## Learning Outcomes - How to build and register custom MCP servers - How to enable communication between agents using stdio and HTTP - How to orchestrate multi-agent workflows with LangChain ## Requirements - Python 3.8+ - See `requirements.txt` for Python dependencies ## License MIT