🧠 MCP Server with LangChain and AI Tools

This project demonstrates how to build a multi-tool AI assistant using the Model Context Protocol (MCP), LangChain, and Groq’s Qwen model. It includes:

• 📐 A local Math MCP Server

• 🌤️ A simulated Weather MCP Server

• 🤖 A conversational AI agent (MCP client) that talks to both

🧰 Features

• Uses LangChain MCP Adapters to connect tools

• Powered by Groq's Qwen LLM

• Handles local and remote tool servers via MCP

• Interactive CLI chat with tool usage detection

Related MCP server: MCP Server For Local

🏁 Prerequisites

• Python >= 3.11

• uv for project/environment management (https://github.com/astral-sh/uv)

• Internet connection for loading LLM (Groq)

⚙️ Setup Instructions

1. Create Project

Set Python version in .python-version and pyproject.toml to >=3.11

2. Create Virtual Environment

3. Add Dependencies

Install them

Project Structure

mcp_project/ │ ├── math_server.py # MCP server for math tools ├── weather_server.py # MCP server for weather API simulation ├── client.py # MCP client with AI agent ├── requirements.txt ├── .python-version └── .env # For storing Groq API key (GROQ_API_KEY)

How to Run

1. Run the Weather Server

2. Run the Client (Automatically runs math server as sub process)

Example Conversation

Note

The weather server is simulated. Replace it with real API logic if needed.

You can add more MCP servers for documents, search, DBs, etc.

Use .env to store your GROQ_API_KEY.