andyWang1688/sql-query-mcp

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

How to use

Install

pipx install sql-query-mcp

Example client config

{\n  "settings": {\n    "default_limit": 200,\n    "max_limit": 1000,\n    "audit_log_path": "logs/audit.jsonl"\n  },\n  "connections": [\n    {\n      "connection_id": "crm_prod_main_ro",\n      "engine": "postgres",\n      "label": "CRM PostgreSQL production / Main / read-only",\n      "env": "prod",\n      "tenant": "main",\n      "role": "ro",\n      "dsn_env": "PG_CONN_CRM_PROD_MAIN_RO",\n      "enabled": true,\n      "default_schema": "public"\n    },\n    {\n      "connection_id": "crm_mysql_prod_main_ro",\n      "engine": "mysql",\n      "label": "CRM MySQL production / Main / read-only",\n      "env": "prod",\n      "tenant": "main",\n      "role": "ro",\n      "dsn_env": "MYSQL_CONN_CRM_PROD_MAIN_RO",\n      "enabled": true,\n      "default_database": "crm"\n    },\n    {\n      "connection_id": "warehouse_hive_prod_main_ro",\n      "engine": "hive",\n      "label": "Warehouse Hive production / Main / read-only",\n      "env": "prod",\n      "tenant": "main",\n      "role": "ro",\n      "dsn_env": "HIVE_CONN_WAREHOUSE_PROD_MAIN_RO",\n      "enabled": true,\n      "default_database": "default"\n    }\n  ]\n}

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.