MCP LangChain Integration Project

A Model Context Protocol (MCP) implementation with multiple servers and LangChain integration for AI agent interactions.

🚀 Project Overview

This project demonstrates how to create MCP servers and integrate them with LangChain agents using the GROQ API. It includes:

• Weather Server: A simple weather service that returns weather information
• Math Server: A mathematical operations service with add and multiply functions
• Client: A LangChain agent that can interact with both servers

📁 Project Structure

MCPLangchain/
├── weather.py          # Weather MCP server
├── mathserver.py       # Math MCP server  
├── client.py           # LangChain client with agent
├── requirements.txt    # Python dependencies
├── .env               # Environment variables (API keys)
└── README.md          # This file

🛠️ Installation

1. Clone and setup the environment:

cd MCPLangchain
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

1. Install dependencies:

pip install -r requirements.txt

1. Set up environment variables: Create a .env file with your GROQ API key:

GROQ_API_KEY="your_groq_api_key_here"

🏃‍♂️ Running the Project

1. Start the Weather Server

python weather.py

The weather server will start on port 8001 with streamable-http transport.

2. Start the Math Server (in a separate terminal)

python mathserver.py

The math server uses stdio transport for direct communication.

3. Run the Client

python client.py

This will:

• Connect to both MCP servers
• Create a LangChain agent with GROQ model
• Execute math and weather queries

🔧 Configuration

Weather Server (weather.py)

• Transport: streamable-http
• Port: 8000
• Function: get_weather(location) - Returns weather info for any location

Math Server (mathserver.py)

• Transport: stdio
• Functions:
  • add(a, b) - Adds two integers
  • multiply(a, b) - Multiplies two integers

Client (client.py)

• Model: GROQ Qwen-QWQ-32B
• Agent Type: React Agent
• Capabilities: Can invoke tools from both servers

📋 Dependencies

• mcp - Model Context Protocol implementation
• langchain-mcp-adapters - LangChain MCP integration
• langchain-groq - GROQ API integration
• langgraph - LangGraph for agent creation
• uvicorn - ASGI server for HTTP transport
• python-dotenv - Environment variable management

🎯 Example Usage

The client demonstrates two main interactions:

1. Math Operations: "What is 100 + 200?"
2. Weather Queries: "What's the weather in Noida?"

Import Errors

Ensure all dependencies are installed:

pip install -r requirements.txt

API Key Issues

Verify your .env file contains the correct GROQ API key:

GROQ_API_KEY="gsk_your_actual_key_here"

🚀 Next Steps

• Add more MCP servers (database, file system, etc.)
• Implement more complex agent workflows
• Add authentication and security features
• Create a web interface for the agent

📚 Resources

• Model Context Protocol (MCP)
• LangChain Documentation
• GROQ API Documentation

Note: This project demonstrates the power of MCP for creating modular, composable AI services that can be easily integrated with LangChain agents.