MySQL Analyzer MCP

A Model Context Protocol (MCP) server for MySQL database performance analysis and optimization.

Overview

MySQL Analyzer MCP is a powerful tool that leverages AI to help database administrators and developers optimize their MySQL databases. It provides comprehensive analysis of database structure, query performance, index usage, and configuration settings, along with actionable recommendations for improvement.

This tool runs as a remote MCP server using Streamable HTTP transport, allowing it to be deployed centrally and accessed by any MCP-compatible client, including Amazon Q Developer CLI, Claude and other AI assistants that support the MCP protocol.

⚠️ Disclaimer

EXPERIMENTAL: This project is experimental and provided as a demonstration of what's possible with MCP and MySQL. All recommendations and code should be carefully reviewed before implementation in any production environment.

NOT OFFICIAL: This is a personal project and not affiliated with, endorsed by, or representative of any organization I work for or contribute to. All opinions and approaches are my own.

NO LIABILITY: This tool is provided "as is" without warranty of any kind. Use at your own risk. The author is not liable for any damages or issues arising from the use of this software.

Features

• Database Structure Analysis: Analyze tables, columns, indexes, and foreign keys
• Query Performance Analysis: Analyze execution plans and identify bottlenecks
• Index Recommendations: Get suggestions for new indexes based on query patterns
• Query Optimization: Receive suggestions for query rewrites to improve performance
• Slow Query Identification: Find and analyze slow-running queries
• InnoDB Buffer Pool Analysis: Analyze buffer pool usage and get optimization recommendations
• Table Fragmentation Analysis: Identify and fix fragmented tables
• MySQL Configuration Review: Review and optimize MySQL settings
• Read-Only Query Execution: Safely execute read-only queries for verification

Security

This tool operates in read-only mode by default. All database connections are established with SET SESSION TRANSACTION READ ONLY to prevent any accidental modifications to your database. The query execution functionality is strictly limited to SELECT, EXPLAIN, and SHOW commands.

Installation

Prerequisites

• Python 3.10+ (or Docker)
• MySQL or MariaDB database
• AWS account (for Secrets Manager, optional)

Setup

Option 1: Local Setup

1. Clone the repository:

   git clone https://github.com/yourusername/mysql-performance-mcp.git
   cd mysql-performance-mcp
   

2. Create virtual environment and Install dependencies:

   python -m venv venv
   source venv/bin/activate 
   pip install -r requirements.txt
   

Option 2: Docker Setup

1. Clone the repository:

   git clone https://github.com/yourusername/mysql-performance-mcp.git
   cd mysql-performance-mcp
   

2. Build the Docker image:

   docker build -t mysql-analyzer-mcp -f Dockerfile .
   

3. Run the Docker container:

   docker run -p 8000:8000 mysql-analyzer-mcp
   

   For AWS credentials (if using Secrets Manager):

   docker run -p 8000:8000 \
     -e AWS_ACCESS_KEY_ID=your_access_key \
     -e AWS_SECRET_ACCESS_KEY=your_secret_key \
     -e AWS_DEFAULT_REGION=your_region \
     mysql-analyzer-mcp
   

4. Configure your database credentials:

   • Option 1: Store credentials in AWS Secrets Manager (recommended)
   • Option 2: Provide credentials directly when using the tools

Usage

Starting the Server

Local:

python main.py --host 0.0.0.0 --port 8000

Docker:

# The server starts automatically when running the container
docker run -p 8000:8000 mysql-analyzer-mcp

Configuring MCP Clients

To connect to your remote MCP server, configure your MCP client with:

Server URL: http://your-server-address:8000/mcp
Transport: Streamable HTTP

Using with an MCP Client

Connect to the server using any MCP-compatible client and use the available tools:

analyze_database_structure(secret_name="my-mysql-db-credentials", region_name="us-west-2")

Available Tools

• analyze_database_structure: Analyze database schema and provide optimization recommendations
• get_slow_queries: Identify slow-running queries in the database
• analyze_query: Analyze a SQL query and provide optimization recommendations
• recommend_indexes: Recommend indexes for a given SQL query
• suggest_query_rewrite: Suggest optimized rewrites for a SQL query
• analyze_innodb_buffer_pool: Analyze InnoDB buffer pool usage and provide optimization recommendations
• analyze_table_fragmentation: Analyze table fragmentation and provide optimization recommendations
• show_mysql_settings: Show MySQL configuration settings with optional filtering
• execute_read_only_query: Execute a read-only SQL query and return the results
• health_check: Check if the server is running and responsive

Customization

The real power of this tool comes from customizing it to your specific environment:

• Add custom analysis rules tailored to your database usage patterns
• Integrate with your monitoring systems
• Customize recommendations based on your organization's best practices
• Add domain-specific knowledge about your data model
• Extend with additional tools specific to your needs

AWS Secrets Manager Setup

To use AWS Secrets Manager for storing database credentials:

1. Create a secret in AWS Secrets Manager with the following keys:

   • host: Database hostname
   • port: Database port (usually 3306)
   • dbname: Database name
   • username: Database username
   • password: Database password

2. Ensure your AWS credentials are configured with appropriate permissions to access the secret.

3. Use the secret name when calling the tools:

   analyze_query(query="SELECT * FROM users WHERE user_id = 123", secret_name="my-mysql-db-credentials")
   

MySQL Configuration

For optimal performance analysis, we recommend enabling the following settings:

-- Enable performance schema
SET GLOBAL performance_schema = ON;

-- Enable slow query log
SET GLOBAL slow_query_log = 'ON';
SET GLOBAL long_query_time = 1;  -- Log queries taking more than 1 second
SET GLOBAL slow_query_log_file = '/var/lib/mysql/slow-queries.log';

And adding these settings to your my.cnf:

[mysqld]
performance_schema = ON
slow_query_log = 1
long_query_time = 1
slow_query_log_file = /var/lib/mysql/slow-queries.log

Examples

Analyzing Database Structure

analyze_database_structure(secret_name="my-mysql-db-credentials")

Analyzing a Query

analyze_query(
    query="SELECT * FROM orders JOIN customers ON orders.customer_id = customers.id WHERE orders.status = 'pending'",
    secret_name="my-mysql-db-credentials"
)

Getting Index Recommendations

recommend_indexes(
    query="SELECT * FROM products WHERE category = 'electronics' AND price < 100",
    secret_name="my-mysql-db-credentials"
)

Analyzing InnoDB Buffer Pool

analyze_innodb_buffer_pool(secret_name="my-mysql-db-credentials")

Executing a Read-Only Query

execute_read_only_query(
    query="SELECT table_name, table_rows FROM information_schema.tables WHERE table_schema = DATABASE() ORDER BY table_rows DESC LIMIT 10",
    secret_name="my-mysql-db-credentials"
)

Deploying to a Remote Server

To deploy the MCP server to a remote machine:

1. Install Docker on your remote server
2. Copy the project files to the server or clone from your repository
3. Build and run the Docker container:

   docker build -t mysql-analyzer-mcp -f Dockerfile .
   docker run -d -p 8000:8000 mysql-analyzer-mcp
   

4. Consider using a process manager like docker-compose or systemd to ensure the container restarts if the server reboots

For secure access, consider setting up:

• A reverse proxy with SSL/TLS (like Nginx or Traefik)
• Authentication middleware
• Firewall rules to restrict access

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.

Acknowledgements

• MySQL community for their documentation
• MCP protocol developers for enabling AI-powered tools
• This project was created using a hybrid approach that combines structured code to extract metrics, execution plans, and database statistics with AI capabilities to provide insightful analysis and recommendations.