SQLite MCP Server

A Model Context Protocol (MCP) server that provides comprehensive SQLite database management and analysis capabilities. This server allows LLMs to explore database schemas, query data, perform updates, and conduct statistical analysis.

Features

• Schema Exploration

  • List all tables in the database
  • View detailed schema information for specific tables
  • Examine column types and constraints

• Data Management

  • Execute read-only SQL queries
  • Perform data modifications (UPDATE, INSERT, DELETE)
  • Safe execution with error handling

• Data Analysis

  • Basic statistical analysis (row counts, null counts, numeric stats)
  • Detailed analysis including categorical data distributions
  • Automatic type detection and appropriate statistical measures

Prerequisites

• Python 3.8 or higher
• SQLite database file
• Claude Desktop (optional, for desktop integration)

Installation

1. First, ensure you have the required Python packages:

pip install mcp pandas

2. Download the SQLite MCP server script:

# Clone this repository or download sqlite_mcp.py directly
curl -O https://raw.githubusercontent.com/yourusername/sqlite-mcp/main/sqlite_mcp.py

3. For Claude Desktop integration:

# Install using MCP CLI
mcp install sqlite_mcp.py --name "SQLite Explorer" --env DB_PATH=/path/to/your/database.sqlite

Usage

• Locate the claude_desktop_config.json file and add below to the mcpServers section
• change the paths to the correct ones for your system.
• Set database location in DB_PATH variable in the .env file.

"sqlite_mcp": {
    "command": "C:\\path\\to\\python.exe",
    "args": [
    "C:\\path\\to\\sqlite-mcp\\server.py"
    ]
}

Available Resources

The server exposes the following MCP resources:

• schema://tables

  • Lists all available tables in the database
  • Example response:

    Available tables:
    - users
    - products
    - orders
    

• schema://{table}

  • Returns detailed schema information for a specific table
  • Example response:

    Table: users
    
    Create Statement:
    CREATE TABLE users (
        id INTEGER PRIMARY KEY,
        name TEXT NOT NULL,
        email TEXT UNIQUE
    )
    
    Columns:
    - id (INTEGER) NOT NULL PRIMARY KEY
    - name (TEXT) NOT NULL
    - email (TEXT)
    

Available Tools

query

Execute read-only SQL queries:

SELECT * FROM users LIMIT 5

update_data

Perform data modifications:

INSERT INTO users (name, email) VALUES ('John Doe', 'john@example.com')

UPDATE users SET email = 'new@example.com' WHERE id = 1

analyze_table

Perform statistical analysis on table data:

Parameters:

• table: Name of the table to analyze
• analysis_type: Either 'basic' or 'detailed'

Example response:

{
  "row_count": 1000,
  "column_count": 5,
  "null_counts": {
    "id": 0,
    "name": 0,
    "email": 15
  },
  "numeric_columns": {
    "id": {
      "mean": 500.5,
      "std": 288.819,
      "min": 1,
      "max": 1000
    }
  }
}

Security Considerations

The server implements several security measures:

1. Input validation for all SQL operations
2. Read-only queries are separated from data modifications
3. Database connection error handling
4. SQL injection protection through parameterized queries

Error Handling

The server provides clear error messages for common issues:

• Database connection failures
• Invalid SQL syntax
• Table not found errors
• Permission issues
• Type mismatches

Contributing

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

License

This project is licensed under the MIT License - see the LICENSE file for details.