mysql-mcp-server-pro-plus 0.2.3

A Model Context Protocol (MCP) server that enables secure interaction with MySQL databases. This server allows AI assistants to list tables, read data, and execute SQL queries through a controlled interface, making database exploration and analysis safer and more structured.

MySQL MCP Server Pro Plus

A robust, secure, and feature-rich Model Context Protocol (MCP) server for MySQL databases. This server provides a standardized interface for AI assistants to interact with MySQL databases through tools and resources.

🚀 Features

Core Features

• Secure Database Operations: Input validation, SQL injection protection, and query sanitization
• Connection Pooling: Efficient connection management with configurable pool settings
• Type Safety: Full type annotations and Pydantic models for configuration validation
• Comprehensive Error Handling: Detailed error messages and proper exception handling
• Async/Await Support: Modern async patterns for better performance
• Resource Management: Proper cleanup of database connections and cursors
• Test Data Generation: Comprehensive e-commerce database simulation with 10M+ rows and bad practices for MCP agent testing

Tools Available

• execute_sql: Execute custom SQL queries with result formatting
• list_tables: List all tables in the database
• describe_table: Get detailed table structure information

Resources

• Table Data: Access table contents as CSV-formatted resources
• Automatic Discovery: Dynamic table listing and resource creation

📋 Requirements

• Python 3.8+
• MySQL 5.7+ or MariaDB 10.2+
• mysql-connector-python

🛠️ Installation

1. Clone the repository:

   git clone <repository-url>
   cd mysql_mcp_server_pro_plus
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Set up environment variables:

   cp env.example .env
   # Edit .env with your MySQL configuration
   

⚙️ Configuration

Environment Variables

Variable	Description	Default	Required
MYSQL_HOST	MySQL server host	localhost	No
MYSQL_PORT	MySQL server port	3306	No
MYSQL_USER	MySQL username	-	Yes
MYSQL_PASSWORD	MySQL password	-	Yes
MYSQL_DATABASE	MySQL database name	-	Yes
MYSQL_CHARSET	Character set	utf8mb4	No
MYSQL_COLLATION	Collation	utf8mb4_unicode_ci	No
MYSQL_AUTOCOMMIT	Auto-commit mode	true	No
MYSQL_SQL_MODE	SQL mode	TRADITIONAL	No
MYSQL_CONNECTION_TIMEOUT	Connection timeout (seconds)	10	No
MYSQL_POOL_SIZE	Connection pool size	5	No
MYSQL_POOL_RESET_SESSION	Reset session on return	true	No

Example Configuration

# .env file
MYSQL_HOST=localhost
MYSQL_PORT=3306
MYSQL_USER=myuser
MYSQL_PASSWORD=mypassword
MYSQL_DATABASE=mydatabase
MYSQL_CHARSET=utf8mb4
MYSQL_COLLATION=utf8mb4_unicode_ci
MYSQL_AUTOCOMMIT=true
MYSQL_SQL_MODE=TRADITIONAL
MYSQL_CONNECTION_TIMEOUT=10
MYSQL_POOL_SIZE=5
MYSQL_POOL_RESET_SESSION=true

📊 Performance

Optimizations

• Connection Pooling: Reuses database connections for better performance
• Async Operations: Non-blocking database operations
• Efficient Query Execution: Optimized query handling and result processing
• Memory Management: Proper cleanup prevents memory leaks

Monitoring

• Comprehensive Logging: Detailed logs for debugging and monitoring
• Error Tracking: Structured error reporting with context
• Performance Metrics: Connection and query performance tracking

🔧 Development

Project Structure

mysql_mcp_server_pro_plus/
├── src/
│   └── mysql_mcp_server_pro_plus/
│       ├── __init__.py
│       └── server.py          # Main server implementation
├── tests/
│   ├── conftest.py           # Test configuration
│   └── test_server.py        # Server tests
├── scripts/
│   └── generate_test_data.py # Test data generator (10M+ rows)
├── init-scripts/
│   └── 01-init.sql          # Database initialization with bad practices
├── docker-compose.yml        # Docker Compose configuration
├── Dockerfile               # Docker image definition
├── pyproject.toml           # Project configuration
├── requirements.txt         # Python dependencies
├── README.md               # This file
└── README-TEST-DATA.md     # Test data generation documentation

Test Data Generation

For comprehensive MCP agent testing, the project includes a sophisticated test data generation system:

• Complex E-commerce Database: 8 interconnected tables with realistic relationships
• 10+ Million Rows: Distributed across users, products, orders, reviews, and payments
• 1 Million Transactions: Mixed SELECT, INSERT, UPDATE, and DELETE operations
• Intentional Bad Practices: Security vulnerabilities, performance issues, and design flaws for MCP agent detection

See README-TEST-DATA.md for detailed documentation.

Quick Start:

# Start the database
make up

# Generate test data (Docker)
make generate-test-data-docker

# Or generate locally
make generate-test-data

# Verify bad practices
make verify-bad-practices-docker

Code Quality

The project follows strict code quality standards:

• Type Annotations: Full type hints for better IDE support and error detection
• Pydantic Models: Data validation and serialization
• Async/Await: Modern Python async patterns
• Error Handling: Comprehensive exception handling
• Documentation: Detailed docstrings and comments

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Run the test suite
6. Submit a pull request

🐛 Troubleshooting

Common Issues

Connection Errors

Error: Missing required database configuration

Solution: Ensure all required environment variables are set (MYSQL_USER, MYSQL_PASSWORD, MYSQL_DATABASE).

Permission Errors

Error: Access denied for user

Solution: Check MySQL user permissions and ensure the user has access to the specified database.

Character Set Issues

Error: Unknown collation

Solution: Update MYSQL_CHARSET and MYSQL_COLLATION to values supported by your MySQL version.

📄 License

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

🤝 Support

For support and questions:

1. Check the troubleshooting section
2. Review the test files for usage examples
3. Open an issue on GitHub
4. Check the CHANGELOG.md for recent updates

🔄 Changelog

See CHANGELOG.md for a detailed history of changes and improvements.