MCP Server with LangChain and AI Tools

# 🧠 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 --- ## 🏁 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 ```bash mkdir mcp_project cd mcp_project uv init ``` Set Python version in .python-version and pyproject.toml to >=3.11 ### 2. Create Virtual Environment ```bash uv venv source .venv/Scripts/activate ``` ### 3. Add Dependencies ``` Create a requirements.txt file: langchain-mcp-adapters langchain-groq langgraph mcp ``` Install them ```bash uv add -r requirements.txt ``` ### 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 ```bash python weather_server.py ``` ### 2. Run the Client (Automatically runs math server as sub process) ```bash python client.py ``` ## Example Conversation ```text You: What is the output of 2*3/(4-2) AI: The result is 3.0 You: What is the weather in New York? AI: The current weather in New York is sunny. You: thanks AI: You're welcome! 😊 ``` ## 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.