About
CentralMind Gateway is a universal database connector that automatically exposes your databases to AI agents via the Model Context Protocol (MCP) or OpenAPI 3.1 protocols. It auto-generates AI-optimized REST APIs and MCP endpoints from your database schema with no manual configuration required. Key features of CentralMind Gateway: - Automatic API generation based on database schema and sample queries - Support for major databases including PostgreSQL, MySQL, ClickHouse, Snowflake, BigQuery, and more - Built-in PII filtering and sensitive data protection for GDPR, CPRA, and SOC 2 compliance - Comprehensive audit logging and traceability for AI agent interactions - Dual protocol support: MCP endpoints for AI agents and OpenAPI/Swagger UI for traditional REST consumers - Enhanced metadata to help AI agents understand database structure and query capabilities - Built-in caching and security optimizations for AI workloads - Works with Docker for easy deployment Designed for development workflows where LLMs need to create, query, or adjust data, as well as analytical scenarios enabling natural language chat with databases and data warehouses.
README
CentralMind Gateway: Create API or MCP Server in Minutes
🚀 Interactive Demo avialable here: https://centralmind.ai
What is Centralmind/Gateway
Simple way to expose your database to AI-Agent via MCP or OpenAPI 3.1 protocols.
docker run --platform linux/amd64 -p 9090:9090 \
ghcr.io/centralmind/gateway:v0.2.18 start \
--connection-string "postgres://db-user:db-password@db-host/db-name?sslmode=require"
This will run for you an API:
INFO Gateway server started successfully!
INFO MCP SSE server for AI agents is running at: http://localhost:9090/sse
INFO REST API with Swagger UI is available at: http://localhost:9090/
Which you can use inside your AI Agent:
Gateway will generate AI optimized API.
Why Centralmind/Gateway
AI agents and LLM-powered applications need fast, secure access to data. We're building an API layer that automatically generates secure, LLM-optimized APIs for your structured data.
It can be useful during development, when an LLM needs to create, adjust, or query data from your database. In analytical scenarios, it enables you to chat with your database or data warehouse. Enrich your AI agents with data from your database using remote function/tool calling.
Features
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