AmanBhattShorthillsAI/Vanna-MCP-Server

Vanna AI MCP Server

This project implements a Model Context Protocol (MCP) server using Vanna AI for natural language to SQL translation and SQL execution over a financial database. It features:

• Natural language to SQL generation using Vanna AI and Azure OpenAI
• SQL execution against a SQLite database
• Full schema and documentation context provided to the LLM for accurate SQL
• Excel logging of all queries, prompts, LLM token usage, cost, timing, and results
• Hot reload workflow for rapid development
• Graceful shutdown and robust error handling

Features

• ask_sql: Converts a natural language question to a SQL query using the LLM, logs all details to query_log.xlsx.
• run_sql: Executes a SQL query and logs execution time and results to Excel.
• LLM token/cost tracking: Logs input/output tokens and estimated cost for each LLM call.
• Signal handling: Clean shutdown on Ctrl+C or kill.
• Hot reload: Easily restart both server and Inspector for rapid iteration.

Setup

1. Clone the repository

git clone <your-repo-url>
cd <your-repo-directory>

2. Install Python dependencies

pip install -r requirements.txt

3. Install Node.js Inspector (optional, for UI)

npm install -g @modelcontextprotocol/inspector

4. Set up environment variables

Create a .env file with your credentials:

OPENAI_API_KEY=your-openai-key
AZURE_OPENAI_ENDPOINT=your-azure-endpoint
WEAVIATE_URL=your-weaviate-url
WEAVIATE_API_KEY=your-weaviate-key

5. Prepare the SQLite database

Place your financial.sqlite database in the project root.

6. (Optional) Train Vanna AI

Run your training script (e.g., train.py) once to populate the vector store.

Usage

Start the MCP server (with hot reload)

pip install watchfiles
watchfiles "uv run mcp dev app.py" .

Start the MCP Inspector (UI)

mcp-inspector

(Optional) Open the Inspector in your browser

open http://localhost:6277  # macOS
# or
xdg-open http://localhost:6277  # Linux

Logging

• All queries, prompts, LLM token usage, cost, timing, and results are logged to query_log.xlsx.
• Each new query appends a row; SQL execution updates the last row with fetch time and result.

Graceful Shutdown

• The server handles SIGINT/SIGTERM for clean shutdown and port release.

Customization

• Adjust LLM cost calculation in calculate_cost() as needed.
• Update schema, documentation, and training data in app.py as your database evolves.

Troubleshooting

• If you see "Not connected" errors in the Inspector, restart both the server and Inspector.
• Ensure all environment variables are set and the database is present.

License

MIT