DataPilot MCP Server

CI/CD Pipeline Coverage Status Python Version License: MIT Code style: black Security: bandit Pre-commit

Navigate your data with AI guidance. A comprehensive Model Context Protocol (MCP) server for interacting with Snowflake using natural language and AI. Built with FastMCP 2.0 and OpenAI integration.

Features

🗄️ Core Database Operations

execute_sql - Execute SQL queries with results
list_databases - List all accessible databases
list_schemas - List schemas in a database
list_tables - List tables in a database/schema
describe_table - Get detailed table column information
get_table_sample - Retrieve sample data from tables

🏭 Warehouse Management

list_warehouses - List all available warehouses
get_warehouse_status - Get current warehouse, database, and schema status

🤖 AI-Powered Features

natural_language_to_sql - Convert natural language questions to SQL queries
analyze_query_results - AI-powered analysis of query results
suggest_query_optimizations - Get optimization suggestions for SQL queries
explain_query - Plain English explanations of SQL queries
generate_table_insights - AI-generated insights about table data

📊 Resources (Data Access)

snowflake://databases - Access database list
snowflake://schemas/{database} - Access schema list
snowflake://tables/{database}/{schema} - Access table list
snowflake://table/{database}/{schema}/{table} - Access table details

📝 Prompts (Templates)

sql_analysis_prompt - Templates for SQL analysis
data_exploration_prompt - Templates for data exploration
sql_optimization_prompt - Templates for query optimization

Related MCP server: Snowflake MCP Service

Installation

Clone and setup the project:
git clone <repository-url> cd datapilot python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate
Install dependencies:
pip install -r requirements.txt
Configure environment variables:
cp env.template .env # Edit .env with your credentials

Configuration

Environment Variables

Create a .env file with the following configuration:

# Required: Snowflake Connection # Account examples: # - ACCOUNT-LOCATOR.snowflakecomputing.com (recommended) # - ACCOUNT-LOCATOR.region.cloud # - organization-account_name SNOWFLAKE_ACCOUNT=ACCOUNT-LOCATOR.snowflakecomputing.com SNOWFLAKE_USER=your_username SNOWFLAKE_PASSWORD=your_password # Optional: Default Snowflake Context SNOWFLAKE_WAREHOUSE=your_warehouse_name SNOWFLAKE_DATABASE=your_database_name SNOWFLAKE_SCHEMA=your_schema_name SNOWFLAKE_ROLE=your_role_name # Required: OpenAI API OPENAI_API_KEY=your_openai_api_key OPENAI_MODEL=gpt-4 # Optional, defaults to gpt-4

Snowflake Account Setup

Get your Snowflake account identifier - Multiple formats supported:
- Recommended: ACCOUNT-LOCATOR.snowflakecomputing.com (e.g., SCGEENJ-UR66679.snowflakecomputing.com)
- Regional: ACCOUNT-LOCATOR.region.cloud (e.g., xy12345.us-east-1.aws)
- Legacy: organization-account_name
Ensure your user has appropriate permissions:
- USAGE on warehouses, databases, and schemas
- SELECT on tables for querying
- SHOW privileges for listing objects

Usage

Running the Server

Method 1: Direct execution

python -m src.main

Method 2: Using FastMCP CLI

fastmcp run src/main.py

Method 3: Development mode with auto-reload

fastmcp dev src/main.py

Connecting to MCP Clients

Claude Desktop

Add to your Claude Desktop configuration:

{ "mcpServers": { "datapilot": { "command": "python", "args": ["-m", "src.main"], "cwd": "/path/to/datapilot", "env": { "SNOWFLAKE_ACCOUNT": "your_account", "SNOWFLAKE_USER": "your_user", "SNOWFLAKE_PASSWORD": "your_password", "OPENAI_API_KEY": "your_openai_key" } } } }

Using FastMCP Client

from fastmcp import Client async def main(): async with Client("python -m src.main") as client: # List databases databases = await client.call_tool("list_databases") print("Databases:", databases) # Natural language to SQL result = await client.call_tool("natural_language_to_sql", { "question": "Show me the top 10 customers by revenue", "database": "SALES_DB", "schema": "PUBLIC" }) print("Generated SQL:", result)

Example Usage

1. Natural Language Query

# Ask a question in natural language question = "What are the top 5 products by sales volume last month?" sql = await client.call_tool("natural_language_to_sql", { "question": question, "database": "SALES_DB", "schema": "PUBLIC" }) print(f"Generated SQL: {sql}")

2. Execute and Analyze

# Execute a query and get AI analysis analysis = await client.call_tool("analyze_query_results", { "query": "SELECT product_name, SUM(quantity) as total_sales FROM sales GROUP BY product_name ORDER BY total_sales DESC LIMIT 10", "results_limit": 100, "analysis_type": "summary" }) print(f"Analysis: {analysis}")

3. Table Insights

# Get AI-powered insights about a table insights = await client.call_tool("generate_table_insights", { "table_name": "SALES_DB.PUBLIC.CUSTOMERS", "sample_limit": 50 }) print(f"Table insights: {insights}")

4. Query Optimization

# Get optimization suggestions optimizations = await client.call_tool("suggest_query_optimizations", { "query": "SELECT * FROM large_table WHERE date_column > '2023-01-01'" }) print(f"Optimization suggestions: {optimizations}")

Architecture

┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ MCP Client │ │ FastMCP │ │ Snowflake │ │ (Claude/etc) │◄──►│ Server │◄──►│ Database │ └─────────────────┘ └─────────────────┘ └─────────────────┘ │ ▼ ┌─────────────────┐ │ OpenAI API │ │ (GPT-4) │ └─────────────────┘

Project Structure

datapilot/ ├── src/ │ ├── __init__.py │ ├── main.py # Main FastMCP server │ ├── models.py # Pydantic data models │ ├── snowflake_client.py # Snowflake connection & operations │ └── openai_client.py # OpenAI integration ├── requirements.txt # Python dependencies ├── env.template # Environment variables template └── README.md # This file

Development

Adding New Tools

Define your tool function in src/main.py:

@mcp.tool() async def my_new_tool(param: str, ctx: Context) -> str: """Description of what the tool does""" await ctx.info(f"Processing: {param}") # Your logic here return "result"

Add appropriate error handling and logging
Test with FastMCP dev mode: fastmcp dev src/main.py

Adding New Resources

@mcp.resource("snowflake://my-resource/{param}") async def my_resource(param: str) -> Dict[str, Any]: """Resource description""" # Your logic here return {"data": "value"}

Troubleshooting

Common Issues

Connection Errors
- Verify Snowflake credentials in .env
- Check network connectivity
- Ensure user has required permissions
OpenAI Errors
- Verify OPENAI_API_KEY is set correctly
- Check API quota and billing
- Ensure model name is correct
Import Errors
- Activate virtual environment
- Install all requirements: pip install -r requirements.txt
- Run from project root directory

Logging

Enable debug logging:

LOG_LEVEL=DEBUG

Contributing

Fork the repository
Create a feature branch
Make your changes
Add tests if applicable
Submit a pull request

License

This project is licensed under the MIT License.

Support

For issues and questions:

Check the troubleshooting section
Review FastMCP documentation: https://gofastmcp.com/
Open an issue in the repository