mcp-ohmy-sql 0.1.4

A powerful Model Context Protocol (MCP) server that provides universal SQL database connectivity with intelligent query optimization, schema introspection, and built-in safety features for AI assistants.

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Welcome to mcp_ohmy_sql Documentation

👀 Overview

mcp_ohmy_sql is a powerful SQL Model Context Protocol (MCP) server that bridges AI assistants with your databases. Built on SQLAlchemy’s robust foundation, it provides universal database connectivity with intelligent query optimization, configurable access controls, and built-in safeguards against excessive data loads to LLMs.

Transform your database interactions with natural language queries, automatic schema discovery, and intelligent result formatting—all while maintaining enterprise-grade security and performance.

See 📚 Full Documentation HERE

🚀 Feature Highlights

Universal Database Support

Connect to virtually any SQL database through SQLAlchemy’s proven architecture. From lightweight SQLite to enterprise PostgreSQL, MySQL, Oracle, and SQL Server—all supported out of the box.

Multi-Database Architecture

Manage multiple databases and schemas simultaneously from a single MCP server. Perfect for complex environments with dev/staging/production databases or multi-tenant applications.

Intelligent Query Optimization

Built-in query analysis engine prevents expensive operations, automatically limits result sets, and provides performance feedback to help you write efficient queries.

AI-Optimized Schema Encoding

Schema information is compressed by ~70% using a specialized encoding format, dramatically reducing token usage while preserving all essential metadata for accurate query generation.

Enterprise-Ready Security

Fine-grained table filtering, parameterized query support, and read-only operations by default. Access controls ensure your production data stays safe.

💎 Why Choose mcp_ohmy_sql?

While other SQL MCP servers exist, mcp_ohmy_sql stands out through:

✨ Comprehensive Database Ecosystem

Beyond traditional SQL databases, we’re expanding to support modern data platforms including AWS Aurora, Redshift, Glue Catalog, MongoDB Atlas SQL, ElasticSearch, OpenSearch, DuckDB, and S3 data files.

🔧 Production-Ready Architecture

Designed for real-world usage with connection pooling, error handling, query timeouts, and result size limits that prevent your LLM conversations from breaking.

📊 Intelligent Result Formatting

Query results are automatically formatted as Markdown tables—the optimal format for LLM comprehension, using 24% fewer tokens than JSON while maintaining perfect readability.

🔒 Security-First Approach

Built-in safeguards include SQL injection prevention, read-only operations, table filtering, and upcoming fine-grained access controls for enterprise deployments.

🎯 Developer Experience

Comprehensive documentation, clear error messages, and extensive configuration options make setup and maintenance straightforward.

Coming Soon: Remote MCP server deployment, advanced access controls, and expanded database ecosystem support.

See our ROADMAP.md for the complete vision and upcoming features.

🚀️ Supported Features

See our ROADMAP.md for the complete vision and upcoming features.

Feature Support Status

Feature	Status	Note
Multi Database Support	✅ Supported
Local MCP Server via UV	✅ Supported
Local MCP Server via Docker	⏳ In Progress
Remote MCP Server	⏳ In Progress
One Click to Deploy Remote MCP Server	⏳ In Progress
Export Results to Local Files	⏳ In Progress
Local Data File Analysis	⏳ In Progress
User Management	⏳ In Progress	Remote MCP server only feature
Access Control Management	⏳ In Progress	Remote MCP server only feature

🛢️ Supported Databases

See our ROADMAP.md for the complete vision and upcoming features.

Database Support Status

Database	Status	Note
Sqlite	✅ Supported	via Sqlalchemy
Postgres	✅ Supported	via Sqlalchemy
MySQL	✅ Supported	via Sqlalchemy
Oracle	✅ Supported	via Sqlalchemy
MSSQL	✅ Supported	via Sqlalchemy
AWS Aurora	⏳ In Progress	via boto3
AWS Redshift	✅ Supported	via boto3
AWS Glue Catalog Databases	⏳ In Progress	via boto3
MongoDB	⏳ In Progress	via Atlas SQL
ElasticSearch	⏳ In Progress	via ElasticSearch SQL
OpenSearch	⏳ In Progress	via OpenSearch SQL
DuckDB	⏳ In Progress	via duckdb
Data Files on AWS S3	⏳ In Progress	via boto3

🎯 Get Started

• Quick Start Guide: Set up and run the server in under 5 minutes