-
securityF
license-
qualityA server that allows AI models to interact with PostgreSQL databases through a standardized protocol, providing database schema information and SQL query execution capabilities.
Last updated -
1
Allows deployment of the chatbot backend as a serverless function, with CloudWatch integration for logging and API Gateway for endpoint exposure.
Supports containerized deployment of the chatbot backend with Docker, including environment variable configuration.
Implements a REST API with CORS support and structured JSON responses for easy frontend integration.
Uses OpenAI GPT models to generate and execute SQL queries based on natural language questions, then summarizes results in structured format.
Connects to a Supabase Postgres database to execute SQL queries generated from user questions and retrieve relevant data.
MCP Chat Backend
This project is a serverless FastAPI backend for a chatbot that generates and executes SQL queries on a Postgres database using OpenAI's GPT models, then returns structured, UI-friendly responses. It is designed to run on AWS Lambda via AWS SAM, but can also be run locally or in Docker.
Features
FastAPI REST API with a single
/askendpointUses OpenAI GPT models to generate and summarize SQL queries
Connects to a Postgres (Supabase) database
Returns structured JSON responses for easy frontend rendering
CORS enabled for frontend integration
Deployable to AWS Lambda (SAM), or run locally/Docker
Verbose logging for debugging (CloudWatch)
Project Structure
├── main.py # Main FastAPI app and Lambda handler
├── requirements.txt # Python dependencies
├── template.yaml # AWS SAM template for Lambda deployment
├── samconfig.toml # AWS SAM deployment config
├── Dockerfile # For local/Docker deployment
├── .gitignore # Files to ignore in git
└── .env # (Not committed) Environment variables
Setup
1. Clone the repository
git clone <your-repo-url>
cd mcp-chat-3
2. Install Python dependencies
python -m venv .venv
source .venv/bin/activate # or .venv\Scripts\activate on Windows
pip install -r requirements.txt
3. Set up environment variables
Create a .env file (not committed to git):
OPENAI_API_KEY=your-openai-key
SUPABASE_DB_NAME=your-db
SUPABASE_DB_USER=your-user
SUPABASE_DB_PASSWORD=your-password
SUPABASE_DB_HOST=your-host
SUPABASE_DB_PORT=your-port
Running Locally
With Uvicorn
uvicorn main:app --reload --port 8080
With Docker
docker build -t mcp-chat-backend .
docker run -p 8080:8080 --env-file .env mcp-chat-backend
Deploying to AWS Lambda (SAM)
Install AWS SAM CLI
Build and deploy:
sam build
sam deploy --guided
Configure environment variables in
template.yamlor via the AWS Console.The API will be available at the endpoint shown after deployment (e.g.
https://xxxxxx.execute-api.region.amazonaws.com/Prod/ask).
API Usage
POST /ask
Body:
{ "question": "your question here" }Response: Structured JSON for chatbot UI, e.g.
{
"messages": [
{
"type": "text",
"content": "Sample 588 has a resistance of 1.2 ohms.",
"entity": {
"entity_type": "sample",
"id": "588"
}
},
{
"type": "list",
"items": ["Item 1", "Item 2"]
}
]
}
See
main.pyfor the full schema and more details.
Environment Variables
OPENAI_API_KEY: Your OpenAI API keySUPABASE_DB_NAME,SUPABASE_DB_USER,SUPABASE_DB_PASSWORD,SUPABASE_DB_HOST,SUPABASE_DB_PORT: Your Postgres database credentials
Development Notes
All logs are sent to stdout (and CloudWatch on Lambda)
CORS is enabled for all origins by default
The backend expects the frontend to handle the structured response format
License
MIT (or your license here)
This server cannot be installed
Related Resources
Related MCP Servers
- AsecurityAlicenseAqualityEnables querying documents through a Langflow backend using natural language questions, providing an interface to interact with Langflow document Q\&A flows.Last updated -114MIT License
- -securityFlicense-qualityA FastMCP server that enables natural language querying of PostgreSQL databases through LLM integration, allowing users to generate SQL queries from plain English and visualize the results.Last updated -3
- -securityAlicense-qualityA Node.js package that provides a Model Context Protocol server for converting natural language questions into SQL queries using AI-powered multi-agent systems.Last updated -62MIT License