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CentralMind Gateway: Create API or MCP Server in Minutes

🚀 Interactive Demo via GitHub Codespaces

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:v0.2.6 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, but traditional APIs and databases aren't built for this purpose. We're building an API layer that automatically generates secure, LLM-optimized APIs for your structured data.

Our solution:

• Filters out PII and sensitive data to ensure compliance with GDPR, CPRA, SOC 2, and other regulations
• Adds traceability and auditing capabilities, ensuring AI applications aren't black boxes and security teams maintain control
• Optimizes for AI workloads, supporting Model Context Protocol (MCP) with enhanced meta information to help AI agents understand APIs, along with built-in caching and security features

Our primary users are companies deploying AI agents for customer support, analytics, where they need models to access the data without direct SQL access to databases elemenating security, compliance and peformance risks.

Features

• ⚡ Automatic API Generation – Creates APIs automatically using LLM based on table schema and sampled data
• 🗄️ Structured Database Support – Supports PostgreSQL, MySQL, ClickHouse, Snowflake, MSSQL, BigQuery, Oracle Database, SQLite, ElasticSearch
• 🌍 Multiple Protocol Support – Provides APIs as REST or MCP Server including SSE mode
• 📜 API Documentation – Auto-generated Swagger documentation and OpenAPI 3.1.0 specification
• 🔒 PII Protection – Implements regex plugin or Microsoft Presidio plugin for PII and sensitive data redaction
• ⚡ Flexible Configuration – Easily extensible via YAML configuration and plugin system
• 🐳 Deployment Options – Run as a binary or Docker container with ready-to-use Helm chart
• 🤖 Multiple AI Providers Support - Support for OpenAI, Anthropic, Amazon Bedrock, Google Gemini & Google VertexAI
• 📦 Local & On-Premises – Support for self-hosted LLMs through configurable AI endpoints and models
• 🔑 Row-Level Security (RLS) – Fine-grained data access control using Lua scripts
• 🔐 Authentication Options – Built-in support for API keys and OAuth
• 👀 Comprehensive Monitoring – Integration with OpenTelemetry (OTel) for request tracking and audit trails
• 🏎️ Performance Optimization – Implements time-based and LRU caching strategies

How it Works

1. Connect & Discover

Gateway connects to your structured databases like PostgreSQL and automatically analyzes the schema and data samples to generate an optimized API structure based on your prompt. LLM is used only on discovery stage to produce API configuration. The tool uses AI Providers to generate the API configuration while ensuring security through PII detection.

2. Deploy

Gateway supports multiple deployment options from standalone binary, docker or Kubernetes. Check our launching guide for detailed instructions. The system uses YAML configuration and plugins for easy customization.

3. Use & Integrate

Access your data through REST APIs or Model Context Protocol (MCP) with built-in security features. Gateway seamlessly integrates with AI models and applications like LangChain, OpenAI and Claude Desktop using function calling or Cursor through MCP. You can also setup telemetry to local or remote destination in otel format.

Documentation

Getting Started

• Quickstart Guide
• Installation Instructions
• API Generation Guide
• API Launch Guide

Additional Resources

• ChatGPT Integration Guide
• Database Connector Documentation
• Plugin Documentation

How to Build

# Clone the repository
git clone https://github.com/centralmind/gateway.git

# Navigate to project directory
cd gateway

# Install dependencies
go mod download

# Build the project
go build .

API Generation

Gateway uses LLM models to generate your API configuration. Follow these steps:

1. Choose one of our supported AI providers:

• OpenAI and all OpenAI-compatible providers
• Anthropic
• Amazon Bedrock
• Google Vertex AI (Anthropic)
• Google Gemini

Google Gemini provides a generous free tier. You can obtain an API key by visiting Google AI Studio:

• Google AI Studio

Once logged in, you can create an API key in the API section of AI Studio. The free tier includes a generous monthly token allocation, making it accessible for development and testing purposes.

Configure AI provider authorization. For Google Gemini, set an API key.

export GEMINI_API_KEY='yourkey'

2. Run the discovery command:

./gateway discover \
  --ai-provider gemini \
  --connection-string "postgresql://neondb_owner:MY_PASSWORD@MY_HOST.neon.tech/neondb?sslmode=require" \
  --prompt "Generate for me awesome readonly API"

3. Monitor the generation process:

INFO 🚀 API Discovery Process
INFO Step 1: Read configs
INFO ✅ Step 1 completed. Done.

INFO Step 2: Discover data
INFO Discovered Tables:
INFO   - payment_dim: 3 columns, 39 rows
INFO   - fact_table: 9 columns, 1000000 rows
INFO ✅ Step 2 completed. Done.

# Additional steps and output...

INFO ✅ All steps completed. Done.

INFO --- Execution Statistics ---
INFO Total time taken: 1m10s
INFO Tokens used: 16543 (Estimated cost: $0.0616)
INFO Tables processed: 6
INFO API methods created: 18
INFO Total number of columns with PII data: 2

4. Review the generated configuration in gateway.yaml:

api:
  name: Awesome Readonly API
  description: ''
  version: '1.0'
database:
  type: postgres
  connection: YOUR_CONNECTION_INFO
  tables:
    - name: payment_dim
      columns: # Table columns
      endpoints:
        - http_method: GET
          http_path: /some_path
          mcp_method: some_method
          summary: Some readable summary
          description: 'Some description'
          query: SQL Query with params
          params: # Query parameters

Running the API

Run locally

./gateway start --config gateway.yaml rest

Docker Compose

docker compose -f ./example/simple/docker-compose.yml up

MCP Protocol Integration

Gateway implements the MCP protocol for seamless integration with Claude and other tools. For detailed setup instructions, see our Claude integration guide.

1. Build the gateway binary:

go build .

2. Configure Claude Desktop tool configuration:

{
  "mcpServers": {
    "gateway": {
      "command": "PATH_TO_GATEWAY_BINARY",
      "args": ["start", "--config", "PATH_TO_GATEWAY_YAML_CONFIG", "mcp-stdio"]
    }
  }
}

Roadmap

It is always subject to change, and the roadmap will highly depend on user feedback. At this moment, we are planning the following features:

Database and Connectivity

• 🗄️ Extended Database Integrations - Redshift, S3 (Iceberg and Parquet), Oracle DB, Microsoft SQL Server, Elasticsearch
• 🔑 SSH tunneling - ability to use jumphost or ssh bastion to tunnel connections

Enhanced Functionality

• 🔍 Advanced Query Capabilities - Complex filtering syntax and Aggregation functions as parameters
• 🔐 Enhanced MCP Security - API key and OAuth authentication

Platform Improvements

• 📦 Schema Management - Automated schema evolution and API versioning
• 🚦 Advanced Traffic Management - Intelligent rate limiting, Request throttling
• ✍️ Write Operations Support - Insert, Update operations