About Clone the repository
A modular backend project combining PostgreSQL, uv Python, and AutoGen AI for developers working on data processing and AI-assisted operations. It supports schema-based validation and transformation plus natural-language database interaction; the README does not describe implementation of the Model Context Protocol.
Key features
- 01Schema-based data validation and transformation
- 02AI-assisted natural-language database interaction
- 03Dependency setup with uv
- 04Modular structure for extension
Use cases
- 01Validating and transforming imported data
- 02Interacting with a database in natural language
- 03Generating SQL queries with AI agents
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEMCP Server with PostgreSQL, uV Python, and AutoGen AI Overview This project is a modular backend server built with uV Python, powered by PostgreSQL for data storage and integrated with AutoGen AI for intelligent data processing and autonomous task execution. The core module, MCP (Modular Command Processor), handles structured interactions between the database and AI agents, supporting real-time operations, import validation, and AI-augmented decision logic.
Key Components uV Python: Lightweight, isolated Python environment used for fast, reproducible deployments.
PostgreSQL: Robust relational database for structured data storage and querying.
MCP (Modular Command Processor): Core logic engine to orchestrate data imports, validation, and transformations using schema-driven rules.
AutoGen AI: Integrated to augment operations with AI capabilities such as natural language query processing, autonomous task execution, and intelligent agent support.
Features Schema-based data validation and transformation
AI-assisted natural language interaction with the database
Streamlined setup using pyproject.toml and uv
Clean modular structure for extensibility and maintainability
Getting Started Prerequisites Python 3.10+
uV (installed via pip: pip install uv)
PostgreSQL running locally or remotely
Setup Instructions
Clone the repository
git clone https://github.com/shrishailwali/mcp-server-postgres
Install dependencies using uV
uv pip install -r requirements.txt # or use pyproject.toml directly Environment Configuration Create a .env file with the following keys:
env
POSTGRES_HOST=localhost POSTGRES_PORT=5432 POSTGRES_USER=postgres POSTGRES_DB=cmms POSTGRES_PASSWORD=yourpassword
Run the Server uvicorn main:app --reload
Integration with AutoGen AI AutoGen is used to:
Parse user input (e.g., file uploads, commands)
Generate SQL queries via AI agents
Automate backend workflows with LLM-powered reasoning
AutoGen integration uses your Azure OpenAI deployment named spick-ai to generate responses and perform actions.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.