SQL_MCP_Server

SQLGenius is an intelligent SQL assistant that helps you query your BigQuery database using natural language. Built with MCP (Model Context Protocol), Vertex AI's Gemini Pro, and Streamlit.

• Natural language to SQL conversion using Gemini Pro
• Interactive Streamlit UI with multiple tabs
• Real-time query execution and visualization
• Database schema explorer
• Query history tracking
• Safe query validation
• BigQuery integration
• MCP-based architecture

Watch SQLGenius in action! Here's a quick demo of how to use the application:

In this demo, you can see:

1. Natural language query conversion to SQL
2. Interactive data visualization
3. Schema exploration
4. Query history tracking

1. Clone the repository and navigate to the project directory:

cd sql_mcp_server

2. Install dependencies:

pip install -r requirements.txt

3. Copy the .env.example file to .env and fill in your configuration:

cp .env.example .env

4. Set up your environment variables in .env:

PROJECT_ID=your-project-id
DATASET_ID=your-dataset-id
GOOGLE_APPLICATION_CREDENTIALS=path/to/your/service-account.json
VERTEX_AI_LOCATION=us-central1

1. Start the application:

streamlit run streamlit_app.py

2. The MCP server will start automatically when the Streamlit app launches

3. Use the tabs to:

   • Ask natural language questions about your data
   • Write SQL queries directly
   • Explore your database schema

Ask questions in plain English and get SQL results:

• "Show me the top 5 customers by revenue"
• "What products have the highest sales in January?"
• "How many orders were placed last month?"

Write and execute SQL queries directly:

SELECT * FROM orders 
WHERE order_date > '2023-01-01' 
ORDER BY total_amount DESC
LIMIT 10

• Browse available tables
• View table schemas
• See sample data from any table

• Only SELECT queries are permitted
• Query validation to prevent dangerous operations
• Secure credential management
• Error handling and input validation

SQLGenius uses the Model Context Protocol (MCP) to expose tools that enable:

1. Natural Language Processing: Convert English questions to SQL
2. Data Exploration: Fetch schema information and sample data
3. SQL Execution: Run validated queries against your database

The architecture consists of:

• MCP Server: Handles DB connection and provides tools
• Streamlit Frontend: User interface for interacting with the system
• Vertex AI (Gemini Pro): Powers natural language understanding
• BigQuery: Executes SQL queries on your data

The following MCP tools are available:

1. execute_nl_query: Execute a natural language query
2. execute_sql_query: Execute a raw SQL query
3. list_tables: List all available tables
4. get_table_schema: Get schema for a specific table

To add custom tools to the MCP server:

1. Edit the register_tools() method in sql_mcp_server.py
2. Add your custom tool using the @self.tool() decorator
3. Restart the server

Contributions are welcome! Please feel free to submit a Pull Request.