MySQL MCP Server

The MySQL Readonly MCP Server is a lightweight, secure gateway that exposes MySQL databases to AI assistants through the Model Context Protocol. By wrapping a standard relational database in an MCP interface, it allows Claude and similar agents to perform ad‑hoc queries without needing direct database credentials or exposing the underlying schema. This solves a common pain point for data‑centric teams: how to give an AI assistant instant, read‑only access to production data while preserving strict security and compliance controls.

At its core, the server implements a set of readonly resources that map to SQL SELECT statements. It manages database connections through an efficient connection pool, automatically limiting the number of concurrent connections and ensuring that each query is executed safely. The server enforces a strict no‑write policy, blocks multi‑statement execution, and requires all queries to be parameterized. These measures prevent accidental data modification or injection attacks, making the server suitable for use in regulated environments where auditability and least‑privilege access are mandatory.

Key capabilities include:

• Automatic result capping: By default, queries return no more than 20 rows, with the limit configurable via an environment variable. This protects downstream systems from runaway queries and keeps response times predictable.
• Formatted output: Results are returned in a structured JSON format that preserves column names and data types, enabling downstream AI pipelines to consume the data without additional parsing.
• Resource description file: A lightweight YAML or JSON file can describe the purpose of tables, columns, and relationships. This metadata is surfaced to the AI model, improving its ability to generate contextually relevant queries and interpret results.
• Connection pooling: The server leverages to pool connections, reducing latency for repeated queries and ensuring efficient use of database resources.

Typical use cases span from data exploration to automated reporting. A developer can embed the MCP server into a chatbot that answers questions like “Show me the latest 10 sales records for product X” or “What is the average order value in Q2?” The AI assistant crafts a parameterized SELECT, sends it via MCP, and receives a clean JSON payload ready for presentation. In research settings, the server can serve as a sandboxed data source for training or fine‑tuning models on real-world schemas without risking exposure of sensitive tables.

Integration into existing AI workflows is straightforward. The server registers itself as an MCP tool; the AI client lists available resources, selects the desired query interface, and invokes it with parameters. Because all interactions are mediated by MCP, developers can leverage existing authentication, logging, and monitoring infrastructure without modifying the AI codebase. The server’s explicit read‑only guarantee also aligns with audit requirements, allowing teams to maintain compliance while still enabling powerful AI-driven insights.