🚀 MySQL MCP Server — AI-Driven MySQL Control & Diagnostics for LLMs

A fully featured Model Context Protocol (MCP) server for MySQL — designed to empower AI assistants, LLMs (like ChatGPT, Claude, Gemini), and automation tools with deep MySQL database access, diagnostics, and intelligent control.

⚡ Ideal for building AI-powered database agents, DevOps automation, or managing MySQL with natural language.

📆 Repository Info

• GitHub: mcp-mysql-server
• Author: @mukul975
• License: MIT
• Keywords: MySQL, MCP Server, AI MySQL interface, LLM database tool, MySQL automation, chatbot SQL

🧠 Key Features

This MySQL MCP Server provides LLMs and AI tools with:

• ✅ Secure Query Execution: Handles SELECT, INSERT, UPDATE, DELETE, etc. with read-only validation.
• 🔍 Schema Introspection: List tables, inspect structures, indexes, keys.
• 📊 Performance Diagnostics: Analyze fragmentation, slow queries, buffer pool stats.
• 🛡️ Security Auditing: Manage users, audit SSL, monitor roles & privileges.
• 🧩 Backup & Replication Monitor: View replication lag, log status, recovery readiness.
• ⚙️ System Monitoring: Get process list, memory usage, plugin state.
• 📈 Query Insights: Index recommendations, event scheduler overview.

🛠️ Installation

```bash pip install -r requirements.txt

Or install manually:

pip install "mcp[cli]" pip install mysql-connector-python ```

⚙️ Environment Configuration

| Variable | Description | Default | | ---------------- | --------------------- | ----------- | | MYSQL_HOST | MySQL server hostname | localhost | | MYSQL_PORT | MySQL port | 3306 | | MYSQL_USER | Username | root | | MYSQL_PASSWORD | Password | (empty) | | MYSQL_DATABASE | Target DB | (empty) |

💡 Use .env or export variables manually

▶️ Run & Usage

bash python mysql_server.py # Default (stdin transport) python mysql_server.py --transport sse # For web clients (SSE) python mysql_server.py --help # Command help

🔗 Claude Desktop Integration

json { "mcpServers": { "mysql": { "command": "python", "args": ["path/to/mysql_server.py"], "env": { "MYSQL_HOST": "localhost", "MYSQL_PORT": "3306", "MYSQL_USER": "your_user", "MYSQL_PASSWORD": "your_pass", "MYSQL_DATABASE": "your_db" } } } }

🧪 Example Workflows

• list_tables: Lists all tables
• describe_table: Schema of a specific table
• execute_sql: Run select or data modification queries
• mysql_slow_query_analysis: Detect slow queries
• mysql_user_privileges: Audit user access
• mysql_replication_lag_monitoring: Check lag in replication

🧰 Toolset Highlights

Access tools via code or LLM prompts:

• mysql_query, list_mysql_tables, mysql_table_schema
• mysql_index_optimization_suggestions, mysql_deadlock_detection
• mysql_ssl_tls_configuration_audit, mysql_backup_health_check
• mysql_server_health_dashboard, mysql_event_scheduler, and dozens more

🔐 Security Considerations

• Store secrets in env vars or vault
• Do not expose server publicly
• Privilege validation and SSL audit included

🧪 Error Handling

• Connection errors
• SQL syntax issues
• Network timeouts
• Returns structured error response

💡 Development Structure

text mcp-mysql-server/ ├── mysql_server.py # Entry point ├── requirements.txt # Dependencies ├── README.md # Docs └── pyproject.toml # Project metadata (optional)

✅ Testing Steps

1. Ensure MySQL is running
2. Set environment vars
3. Run python mysql_server.py
4. Try with mcp dev mysql_server.py (if using MCP CLI)

🤝 Contributing

• Fork the repo
• Create feature/bug branches
• Submit PRs with description & tests

📄 FAQ

Q: What is MCP? A: Model Context Protocol (MCP) is an interface to give LLMs access to structured tools like databases, APIs, and system utilities.

Q: Can I use this with ChatGPT or Claude? Yes! It's designed for direct integration with AI/LLM tools that support tool-use or system-level automation.

Q: Is it safe to run this in production? It depends on your environment. Always restrict access, use read-only roles, and monitor logs.

📄 License

MIT License — Open source and free to use.

🔎 GitHub SEO Tips (apply on GitHub)

📣 Promote It

• Share on Reddit r/MachineLearning
• Post to LinkedIn or Twitter with GIF or screenshots
• Submit to awesome-LLM or similar curated lists

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