MySQL MCP Server
hybrid server
The server is able to function both locally and remotely, depending on the configuration or use case.
Integrations
Provides environment variable management for configuring database credentials and server settings through .env files.
Supports containerized deployment of the MCP server through Docker, allowing configuration of database connection parameters and port mappings.
Includes Mermaid diagram support for visualizing the server architecture and data flow between components.
mysql-mcp-server
0. Execution
Running with Docker
Change the database connection information as needed.
Running with Docker Compose
This will proceed with a pre-configured setup.
Running directly with Python
Cursor Configuration
MCP functionality is available from Cursor version 0.46 and above.
Additionally, the MCP feature is only accessible to Cursor Pro account users.
Tool Addition Tips
- Adding a Tool
execute
functions implement the actual logic execution (Service Layer).- The
@tool
decorator helps register the tool with MCP (Controller Layer).
- Explanation
- Each file under
mysql_mcp_server/executors
represents a single tool. - If a new tool is added, it must be imported in
mysql_mcp_server/executors/__init__.py
and included in the__all__
array. - This ensures the module is automatically registered in the
TOOLS_DEFINITION
variable.
- Each file under
š§ Development Roadmap š§
- āļø Parameter Options
- š§ Enable/Disable Switch for Each Tool: Provide a function to reduce Input Context costs š°
- š Query Security Level Setting: Offer optional control over functions that could damage asset value, such as DROP, DELETE, UPDATE š«
- āØ Features
- š Data Analysis Report Generation: Provide a report generation function optimized for the model to appropriately select various charts based on user requests š
- š Reporting capabilities for prescribed forms
- šļø Diversify report templates
- šļø Extended Text2SQL Support
- š SSH Connection Support: Enable secure remote access via SSH for advanced operations š
- š„ File Extraction Function
- š CSV
- š JSON
- š Excel
- š Data Analysis Report Generation: Provide a report generation function optimized for the model to appropriately select various charts based on user requests š
1. Overview
MCP MySQL Server is a server application for MySQL database operations based on MCP (Model Context Protocol). This server provides tools that allow AI models to interact with the MySQL database.
2. System Configuration
2.1 Key Components
- MCP Server: A FastMCP server that communicates with AI models
- MySQL Database: Manages and stores data
- Tools: Executors that perform database operations
2.2 Tech Stack
- Language: Python
- Database: MySQL 8.0
- Key Libraries:
- mcp: Implements Model Context Protocol for AI communication
- PyMySQL: Connects to MySQL and executes queries
- pandas: Processes and analyzes data
- python-dotenv: Manages environment variables
- fire: Implements command-line interfaces
2.3 Deployment Environment
- Containerized deployment via Docker and Docker Compose
- Ports: 8081 (MCP Server), 3306 (MySQL)
3. Directory Structure
4. Architecture Design
4.1 Layered Structure
- Interface Layer: MCP Server (FastMCP)
- Business Logic Layer: Handlers and Executors
- Data Access Layer: Database connection and query execution
4.2 Key Classes and Modules
- MySQLMCPServer: Main server class that initializes and runs the MCP server
- DatabaseManager: Singleton pattern-based database connection manager
- Executors: Collection of tools for database operations
- execute_create_table: Creates tables
- execute_desc_table: Checks table schema
- execute_explain: Provides query execution plans
- execute_insert_query: Executes INSETR queries
- execute_select_query: Executes SELECT queries
- execute_show_tables: Retrieves table lists
4.3 Communication Flow
- AI model requests a list of available tools from the MCP server.
- The server returns the available tools list.
- The AI model requests the execution of a specific tool.
- The server calls the corresponding executor to perform the database operation.
- The execution results are returned to the AI model.
5. Scalability and Maintenance
- Adding Tools: Implement new tools in the
executors
directory and register them in__init__.py
. - Environment Configuration: Manage environment variables via the
.env
file. - Logging: Ensure consistent logging using
logger_helper
.
6. Deployment and Execution
6.1 Local Execution
6.2 Docker Deployment
7. Security Considerations
- Manage database credentials via environment variables.
- Use strong passwords in production environments.
- Consider implementing SSL/TLS encryption for database connections when necessary.
This server cannot be installed
A server that enables AI models to interact with MySQL databases through a Model Control Protocol, providing tools for table creation, schema inspection, query execution, and data retrieval.
- 0. Execution
- š§ Development Roadmap š§
- 1. Overview
- 2. System Configuration
- 3. Directory Structure
- 4. Architecture Design
- 5. Scalability and Maintenance
- 6. Deployment and Execution
- 7. Security Considerations