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andyWang1688/sql-query-mcp

by Various

A general-purpose MCP server that lets AI work with multiple databases within clear boundaries.

A

MCP

andyWang1688/sql-query-mcp

Added 1 June 2026

#chatgpt #codex #mcp #mcp-server #model-context-protocol #mysql #postgresql #sql

Overview

A general-purpose MCP server that lets AI work with multiple databases within clear boundaries. It is written in Python and provides a standardized interface for executing SQL queries across different database systems.

Best for

Best for
Developers needing a simple, controlled way to let AI assistants query multiple databases

Use cases

  • Enable AI assistants to query production databases with controlled access
  • Automate database reporting by connecting AI to multiple data sources
  • Build secure database interactions for AI-driven analytics tools

Notes

A general-purpose MCP server that lets AI work with multiple databases within clear boundaries. It is written in Python and provides a standardized interface for executing SQL queries across different database systems.

4 stars on GitHub. Last updated 2026-05-22. Licensed MIT.

Use cases

  • Enable AI assistants to query production databases with controlled access
  • Automate database reporting by connecting AI to multiple data sources
  • Build secure database interactions for AI-driven analytics tools

Pros

  • Supports multiple database types through a single MCP interface
  • Clear boundary enforcement helps prevent unintended data modifications
  • Lightweight Python implementation easy to integrate into existing workflows

Cons

  • Limited community adoption with only 4 stars on GitHub
  • Documentation and examples may be sparse for complex setups
  • Requires manual configuration for each database connection

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Supports multiple database types through a single MCP interface
  • Clear boundary enforcement helps prevent unintended data modifications
  • Lightweight Python implementation easy to integrate into existing workflows

Cons

  • Limited community adoption with only 4 stars on GitHub
  • Documentation and examples may be sparse for complex setups
  • Requires manual configuration for each database connection