mcp-sql

# Hands-on Model Context Protocol (MCP) This project demonstrates how to use the Model Context Protocol (MCP) to create a natural language interface to SQLite databases. It's a practical introduction to building AI systems that can interact with external tools. ## What is MCP? MCP is designed as a universal protocol that connects AI systems with data sources and tools using a standardized communication layer. As described by Anthropic: > "The Model Context Protocol is an open standard that enables developers to build secure, two-way connections between their data sources and AI-powered tools." anthropic.com It functions somewhat like an "Internet for Agents," providing a standardized way for AI systems to access external tools without needing custom integrations for each one. This demo shows how an LLM (Groq's Llama3-70B) can execute SQL queries through MCP, without directly accessing the database. ## Getting Started 1. Make sure you have Python 3.12+ installed 2. Set your Groq API key in the `.env` file: ``` GROQ_API_KEY=your_api_key_here ``` 3. Install dependencies: ``` uv venv .venv --python=3.12 .venv\Scripts\activate uv pip install -e . ``` 4. Run the demo: ``` run_sql_demo.bat ``` This will open two windows - an MCP server and an MCP client. ## Hands-on with MCP In the client window, try natural language queries like: - "all users" - The LLM converts this to a SQL query using MCP's tool interface - "users older than 30" - Watch how complex queries get properly translated - "average age of users" - See aggregation functions at work - "add a new user named Alice Smith with email alice@example.com and age 35" - Try data manipulation Each query demonstrates how MCP enables an LLM to use external tools (SQL queries) safely and effectively. ## How MCP Works in This Project 1. **MCP Server (mcp_server.py)**: - Defines tools (like `query_data`) with schemas - Handles execution of SQL queries against a database - Returns results in a structured format 2. **MCP Client (mcp_client.py)**: - Takes natural language input from users - Uses Groq's LLM to generate tool calls via MCP - Handles the communication protocol with the server The magic happens through MCP's standardized communication, allowing LLMs to use tools without accessing the underlying code. ## Project Structure - `mcp_server.py`: MCP server with SQL execution capabilities - `mcp_client.py`: MCP client for LLM-driven tool calling - `database.db`: Sample SQLite database with user data - `run_sql_demo.bat`: Script to run both MCP components - `pyproject.toml`: Python project configuration - `.env`: Environment variables (API keys)