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Vanna AI MCP Server

by lilgreml1n
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Python
Hybrid

Vanna AI MCP Server

MCP (Model Context Protocol) server that enables AI assistants to query databases using natural language via Vanna AI.

Features

  • 🗣️ Natural Language SQL: Ask questions in plain English, get SQL queries automatically

  • 🔐 Secure SSH Tunneling: Connects to remote databases via SSH with key or password auth

  • 🤖 AI Training: Train Vanna on your database schema and sample queries

  • 🔌 MCP Compatible: Works with Claude Desktop, Gemini CLI, and other MCP clients

  • 📊 ChromaDB Vector Store: Persistent storage for trained SQL patterns

Quick Start

Using Docker (Recommended)

# Create .env file (see .env.sample) cp .env.sample .env # Edit .env with your credentials # Run with Docker Compose docker-compose up -d

Server runs on http://localhost:8000

Local Installation

# Install with uv uv pip install vanna chromadb sshtunnel python-dotenv mcp starlette uvicorn # Run the server uv run python main.py

Configuration

Create a .env file with these variables:

# SSH Tunnel Configuration SSH_HOST=your-server.com SSH_PORT=22 SSH_USERNAME=your-user SSH_PASSWORD=your-password # OR use key-based auth SSH_KEY_PATH=/path/to/private/key # Optional SSH_KEY_PASSPHRASE=key-passphrase # Optional # MySQL Database (on remote server) MYSQL_REMOTE_HOST=localhost # Usually localhost when using tunnel MYSQL_REMOTE_PORT=3306 MYSQL_DATABASE=your_database MYSQL_USER=db_user MYSQL_PASSWORD=db_password # Vanna AI VANNA_MODEL=chinook # Your model name VANNA_API_KEY=your-api-key # Get from vanna.ai

Available MCP Tools

  • generate_sql - Generate SQL from natural language question

  • run_sql - Execute SQL query and return results

  • train_question_sql - Train Vanna with example question/SQL pairs

  • train_ddl - Train Vanna on database schema (DDL statements)

  • get_training_data - View all trained examples

Usage Examples

With Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{ "mcpServers": { "vanna-ai": { "command": "uv", "args": ["run", "python", "/absolute/path/to/fastmcp/main.py"] } } }

Example Queries

You: Show me the top 5 customers by total sales Claude: [generates and runs SQL query] You: What tables are in the database? Claude: [calls get_training_data to see schema] You: Train this: "show active users" -> SELECT * FROM users WHERE active=1 Claude: [calls train_question_sql]

Project Structure

fastmcp/ ├── main.py # MCP server with Vanna integration ├── docker-compose.yml # Docker deployment ├── Dockerfile ├── pyproject.toml # uv dependencies ├── .env.sample # Configuration template └── chroma_data/ # ChromaDB vector storage (persistent)

Tech Stack

  • Vanna AI: Natural language to SQL conversion

  • ChromaDB: Vector database for training data storage

  • MCP: Model Context Protocol for AI tool integration

  • SSH Tunnel: Secure remote database access

  • FastAPI/Starlette: HTTP server framework

Security Notes

  • Never commit .env file to version control

  • Use SSH key authentication when possible

  • Database credentials are transmitted only through SSH tunnel

  • ChromaDB data is persisted in chroma_data/ directory

License

Personal use. Database access and credentials remain private.

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