PostgreSQL MCP Server

A Model Context Protocol (MCP) server that provides AI assistants with secure access to PostgreSQL databases.

Features

• 🔒 Secure database access with read-only queries by default

• 🛠️ Comprehensive database tools for schema exploration and querying

• 🧠 Intelligent query validation with security and performance analysis

• ⚡ Real-time optimization suggestions for better query performance

• 🎯 SQL injection detection and dangerous operation blocking

• ⚙️ Configurable connection pooling and query limits

• 🔍 Schema filtering for multi-tenant environments

• 📝 Detailed logging and query monitoring

• 🚀 Easy setup with environment variables or config files

Installation

1. Clone the repository:

2. Install dependencies:

3. Configure your database connection (see Configuration)

Configuration

Environment Variables (Recommended)

Copy .env.example to .env and configure your database:

Edit .env with your database credentials:

JSON Configuration (Alternative)

Copy config.example.json to config.json and modify as needed.

Usage

Start the MCP Server

Test Database Connection

Enable Verbose Logging

Demo Query Validation (No Database Required)

This demo script showcases the query validation features without requiring a database connection.

Available Tools

The MCP server provides the following tools for AI assistants with built-in query validation and optimization:

1. query

Execute SELECT queries on the database.

Parameters:

• sql (required): SQL SELECT query to execute

• params (optional): Array of parameters for the query

Example:

2. list_tables

List all tables in a database schema.

Parameters:

• schema (optional): Schema name (default: "public")

Example:

3. describe_table

Get detailed information about a table's columns and structure.

Parameters:

• table_name (required): Name of the table to describe

• schema (optional): Schema name (default: "public")

Example:

4. list_schemas

List all available database schemas.

Example:

5. test_connection

Test the database connection and get server information.

Example:

6. validate_query

Validate and analyze a SQL query for security issues, performance problems, and optimization opportunities.

Parameters:

• sql (required): SQL query to validate and analyze

• schema (optional): Database schema name for validation context (default: "public")

Example:

Features:

• Security Analysis: Detects SQL injection patterns and dangerous operations

• Performance Warnings: Identifies inefficient query patterns

• Optimization Suggestions: Recommends improvements for better performance

• Complexity Scoring: Rates query complexity on a 1-10 scale

• Index Recommendations: Suggests indexes for better performance

Example Response:

Query Validation & Optimization

The MCP server includes intelligent query analysis that automatically validates every query for security and performance issues.

Real-Time Query Analysis

• Automatic validation of all queries before execution

• Security threat detection including SQL injection patterns

• Performance issue identification for slow query patterns

• Optimization suggestions with specific recommendations

• Complexity scoring to help understand query resource usage

Security Validation

• SQL injection detection using pattern matching

• Dangerous function blocking (e.g., pg_read_file, COPY)

• Statement type validation (only SELECT allowed)

• Comment pattern analysis for potential bypass attempts

Performance Analysis

• SELECT * detection with column-specific recommendations

• Missing index suggestions based on WHERE/JOIN clauses

• Cartesian product warnings for JOINs without conditions

• Leading wildcard detection in LIKE patterns

• Query complexity scoring (1-10 scale)

Optimization Suggestions

• Index recommendations for frequently filtered columns

• Query restructuring suggestions for better performance

• LIMIT clause recommendations for large result sets

• JOIN order optimization for complex queries

• EXISTS vs IN recommendations for subqueries

Security Features

Read-Only Queries

By default, only SELECT statements are allowed. This prevents accidental data modification through the MCP server.

Row Limits

All queries are automatically limited to prevent excessive memory usage and long-running queries.

Schema Filtering

You can restrict access to specific database schemas using the MCP_ALLOWED_SCHEMAS configuration.

Connection Pooling

Database connections are managed through a connection pool to ensure efficient resource usage.

Development

Running Tests

Code Formatting

Type Checking

Configuration Options

Database Configuration

Server Configuration

Troubleshooting

Connection Issues

1. Verify your database credentials in .env

2. Ensure PostgreSQL is running and accessible

3. Check firewall and network connectivity

4. Test connection: python main.py --test

Permission Issues

1. Ensure the database user has appropriate SELECT permissions

2. Check schema access permissions

3. Verify SSL configuration if required

Performance Issues

1. Adjust connection pool settings

2. Implement query optimization

3. Consider adding row limits to queries

4. Monitor query execution times

Contributing

1. Fork the repository

2. Create a feature branch

3. Make your changes

4. Add tests for new functionality

5. Submit a pull request

License

MIT License - see LICENSE file for details.