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

Security & Reliability

• Secure Database Operations: Input validation, SQL injection protection, and query sanitization
• Enterprise Security: Advanced security validation with configurable security levels
• Connection Pooling: Efficient connection management with configurable pool settings
• Comprehensive Error Handling: Detailed error messages and proper exception handling
• Resource Management: Proper cleanup of database connections and cursors

Performance & Monitoring

• Async/Await Support: Modern async patterns for better performance
• Enterprise Monitoring: Database health analysis, performance metrics, and alerting
• Query Performance Analysis: EXPLAIN plan analysis and optimization recommendations
• Lock Contention Analysis: Blocking queries detection and resolution guidance
• Schema Visualization: ER diagrams and relationship mapping

Development & Testing

• Type Safety: Full type annotations and Pydantic models for configuration validation
• Comprehensive Testing: Unit tests, security tests, and integration tests
• Test Data Generation: Comprehensive e-commerce database simulation with 10M+ rows and bad practices for MCP agent testing
• Interactive Exploration: Advanced data exploration with drill-down capabilities
• CI/CD Integration: Pre-commit hooks, security scanning, and automated testing

Tools Available

Core Database Tools

• execute_sql: Execute custom SQL queries with result formatting, security validation, and SQL injection protection
• list_tables: List all tables in the database with basic metadata
• describe_table: Get detailed table structure information including columns, indexes, constraints, and statistics

Advanced Analysis Tools

• analyze_db_health: Enterprise-grade database health monitoring covering:

  • Index health and usage statistics
  • Connection pool status and limits
  • Replication status and lag monitoring
  • Buffer pool efficiency analysis
  • Constraint integrity validation
  • Auto-increment sequence analysis
  • Table fragmentation assessment
  • Comprehensive performance metrics

• analyze_query_performance: Query performance analysis and optimization:

  • EXPLAIN plan analysis with execution strategy insights
  • Performance metrics and cost estimation
  • Index recommendations and optimization suggestions
  • Query rewrite suggestions and JOIN optimization
  • Execution analysis with runtime statistics
  • Resource consumption predictions

• get_blocking_queries: Lock contention and blocking queries analysis:

  • Lock wait graph visualization
  • Deadlock detection and prevention
  • Session termination recommendations
  • Lock timeout configuration suggestions
  • Historical blocking analysis
  • MySQL PERFORMANCE_SCHEMA integration

Database Exploration Tools

• explore_interactive: Interactive data exploration with multiple analysis modes:

  • Drill-down exploration capabilities
  • Smart sampling for large datasets
  • Pattern discovery and anomaly detection
  • Relationship navigation and analysis
  • Time-series analysis for temporal data
  • Comparative analysis across tables
  • Data quality assessment

• get_database_overview: Comprehensive database overview:

  • Schema analysis and table relationships
  • Performance and security analysis
  • Statistical sampling for large datasets
  • Data quality metrics and insights
  • Security vulnerability assessment

• get_schema_visualization: Schema visualization and relationship mapping:

  • ER diagram generation (ASCII/text-based)
  • Table dependency analysis
  • Foreign key relationship mapping
  • Constraint visualization (primary keys, unique constraints)
  • Circular reference detection
  • Impact analysis for schema changes

Resources

• Table Data: Access table contents as CSV-formatted resources via mysql://{table_name}/data
• Automatic Discovery: Dynamic table listing and resource creation
• Schema Resources: Database schema information and metadata access

📋 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_URL	MySQL connection URL (preferred)	-	No*
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

Note: Either MYSQL_URL or the individual MYSQL_USER, MYSQL_PASSWORD, and MYSQL_DATABASE variables are required. If MYSQL_URL is provided, it takes precedence over individual variables.

Example Configuration

# .env file

# Option 1: Using MySQL URL (Recommended)
MYSQL_URL=mysql://myuser:mypassword@localhost:3306/mydatabase?charset=utf8mb4&collation=utf8mb4_unicode_ci&sql_mode=TRADITIONAL

# Option 2: Using individual variables
# 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 MCP server implementation
│       ├── config.py              # Configuration management
│       ├── db_manager.py          # Database connection and management
│       ├── logger.py              # Logging configuration
│       ├── schema_mapping.py      # Schema analysis utilities
│       ├── validator.py           # Security validation
│       └── tools/                 # MCP Tools directory
│           ├── __init__.py
│           ├── analyze_db_health.py          # Database health analysis
│           ├── analyze_query_performance.py # Query performance analysis
│           ├── describe_table.py             # Enhanced table description
│           ├── discover_sensitive_data.py    # Sensitive data discovery (reference)
│           ├── execute_sql.py                # SQL execution tool
│           ├── explore_interactive.py        # Interactive data exploration
│           ├── get_blocking_queries.py       # Blocking queries analysis
│           ├── get_database_overview.py      # Database overview tool
│           ├── get_schema_visualization.py   # Schema visualization
│           └── list_tables.py                # Table listing tool
├── tests/
│   ├── conftest.py             # Test configuration
│   └── test_server.py         # Server unit tests
├── scripts/
│   ├── generate_test_data.py   # Test data generator (10M+ rows)
│   ├── security/
│   │   ├── bandit-docker.sh    # Security scanning with Bandit
│   │   ├── check-secrets.sh    # Secret detection
│   │   └── verify_bad_practices.py # Bad practices verification
├── init-scripts/
│   └── 01-init.sql            # Database initialization with bad practices
├── data/
│   └── mysql/                 # MySQL data directory
├── mysql-config/
│   └── my.cnf                 # MySQL configuration
├── hooks/
│   ├── post_gen_project.py    # Post-generation hooks
│   ├── pre-commit-check-dependencies.sh
│   ├── pre-commit-check-secrets.sh
│   └── pre-commit-run-tests.sh
├── dist/                      # Distribution packages
├── logs/                      # Application logs
├── docker-compose.yml         # Docker Compose configuration
├── Dockerfile                 # Docker image definition
├── pyproject.toml            # Project configuration (Poetry)
├── uv.lock                   # Dependency lock file
├── pytest.ini               # Pytest configuration
├── test_security.py          # Security tests
├── Makefile                  # Build automation
├── env.example              # Environment variables template
├── .env                      # Local environment (gitignored)
├── CHANGELOG.md             # Change log
├── LICENSE                  # MIT License
└── README.md                # This file

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

Security Features

The project includes comprehensive security features for production deployment:

Automated Security Scanning

• Bandit Integration: Automated Python security vulnerability scanning
• Secret Detection: Pre-commit hooks to prevent accidental secret commits
• Security Testing: Dedicated security test suite in test_security.py

Security Tools

• Sensitive Data Discovery: Pattern-based detection of PII, financial data, and sensitive information
• Security Validation: Configurable security levels for different deployment environments
• SQL Injection Protection: Advanced query validation and sanitization

Security Scripts

# Run comprehensive security checks
make security-check

# Run security tests (standalone)
python -m pytest test_security.py -v

# Check for secrets in code
./scripts/security/check-secrets.sh

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
• Security Scanning: Automated security vulnerability detection with Bandit
• Pre-commit Hooks: Code quality checks and secret detection
• Comprehensive Testing: Unit tests, integration tests, and security tests

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. Either provide MYSQL_URL or the individual variables (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.