"MySQL" matching MCP servers:

Enables safe MySQL database querying through MCP, allowing natural language to SQL analysis via DeepSeek integration.

Access control for text-to-SQL agents. Before the model sees the schema, schemagate keeps only the tables and columns the caller may read, picks the few a question needs, and refuses hidden ones at query time. Postgres, Oracle, MySQL, SQL Server, SQLite. Quantum-safe signed audit log. Try it in your browser: https://ashishsinha1602.github.io/schemagate/

QueryBridge is a local AI application that connects a MySQL database to an Ollama-powered language model through an MCP (Model Context Protocol) tool server. It lets you ask natural-language questions about your data while keeping database access structured, safe, and local.

Enables MCP clients to manage an e-commerce product catalog in MySQL, including creating, reading, updating, deleting, and searching products, plus AI-generated descriptions and low-stock monitoring.

Enables CRUD operations for e-commerce products with AI-generated descriptions, MySQL-backed search, inventory monitoring resources, and prompt templates over stdio or HTTP/SSE.

Enables AI clients to perform full CRUD operations on e-commerce products stored in MySQL, including search with pagination, AI-powered description generation via sampling, and low-stock inventory monitoring. It also exposes product catalog resources and pre-built prompt templates over both stdio and HTTP/SSE transports.

Enables e-commerce product management with CRUD operations, AI-powered descriptions, and MySQL database integration via MCP.

Enables users to ask plain-English questions against MySQL, PostgreSQL, SQLite, SQL Server, Oracle, and MariaDB databases by auto-detecting schemas and converting requests into safe, read-only SQL queries.

An MCP server that enables AI agents to safely explore and interact with MySQL databases through dynamic tool generation from stored procedures. It provides database discovery capabilities and intelligent procedure categorization while enforcing security restrictions to prevent data modification.

Enables LLM agents to connect to and retrieve data from multiple database types through a unified API, including PostgreSQL, MySQL, SQLite, BigQuery, Oracle, MS SQL, and Redshift. It supports schema normalization and query transformation to make database access more compatible with natural-language workflows.

Enables natural language performance analysis and optimization of AWS Aurora MySQL databases, using AI (Bedrock, Claude) and RAG to generate HTML reports and recommendations.

Enables natural-language Chinese queries across MySQL, Dameng DM, and KingbaseES databases, with safe SQL generation, schema exploration, and read-only query execution.

Enables LLM agents to connect to and retrieve data from a wide range of databases — including PostgreSQL, MySQL, SQLite, BigQuery, Oracle, MS SQL, and Redshift — through a single unified API. It also normalizes schemas for LLM compatibility, optimizes generated queries, and transforms them back into their original form for execution.

Enables AI assistants to query relational databases like MySQL, PostgreSQL, and Oracle using natural-language questions, dynamically discovering schemas and generating validated read-only SQL through configurable AI providers such as Groq, Claude, and Gemini.