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

Related MCP server: MCP Model Provider Example

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:

pip install -r requirements.txt

3. Set up environment variables:

• Create a .env file with your GROQ_API_KEY:

  GROQ_API_KEY=your_groq_api_key_here

4. Run the servers:

• Start the weather server (in one terminal):

  python weather.py

• The math server is started automatically by the client when needed.

5. Run the client:

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