Integrates with SQLite databases to enable AI-driven schema exploration, querying, updates, and statistical analysis for...
Created byApr 23, 2025
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](https://claude.ai/download) (optional, for desktop integration)
Installation
First, ensure you have the required Python packages:
Download the SQLite MCP server script:
For Claude Desktop integration:
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.
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:
update_data
Perform data modifications:
analyze_table
Perform statistical analysis on table data:
Parameters:
`table`: Name of the table to analyze
`analysis_type`: Either 'basic' or 'detailed'
Example response:
Security Considerations
The server implements several security measures:
Input validation for all SQL operations
Read-only queries are separated from data modifications
Database connection error handling
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.