Postgres-MCP-Server-With-SSE-Transport

Postgres-MCP-Server-With-SSE-Transport

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The MCP Server is designed to facilitate flexible AI model integration with secure configuration management and PostgreSQL support. It leverages communication transports like SSE and offers extensibility for tool registration and AI provider configuration.

MCP (Model Context Protocol) Server

Project Structure

├── client.py             # Client-side interaction script
├── server.py             # Main MCP server implementation
├── pg_connect.py         # PostgreSQL database connection
├── lm_config.py          # Language model configuration
│
├── .env.example          # Example environment configuration
├── .env.dev              # Development environment configuration
├── requirements.txt      # Project dependencies
└── .gitignore            # Git ignore file

Prerequisites

  • Python 3.10+
  • PostgreSQL
  • API access to AI providers (Anthropic, Google)

Installation

1. Clone the Repository

git https://github.com/VajraM-dev/Postgres-MCP-Server-With-SSE-Transport.git

2. Create Virtual Environment

python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install Dependencies

pip install -r requirements.txt

4. Configure Environment

  1. Copy .env.example to .env.dev
  2. Fill in the required configuration:
cp .env.example .env.dev
nano .env.dev  # or use your preferred text editor
Configuration Parameters
  • POSTGRES_USERNAME: PostgreSQL database username
  • POSTGRES_PASSWORD: PostgreSQL database password
  • POSTGRES_DB_NAME: Database name
  • POSTGRES_HOST: Database host
  • POSTGRES_PORT: Database port
  • MCP_NAME: Server name
  • MCP_HOST: Server host
  • MCP_PORT: Server port
  • TRANSPORT: Communication transport (sse/stdio)
  • ANTHROPIC_API_KEY: Anthropic API key
  • GOOGLE_API_KEY: Google API key
  • USE_PROVIDER: Default AI provider

Running the Server

Development Mode

python server.py

Client Interaction

python client.py

Key Features

  • 🔒 Secure configuration management
  • 🗃️ PostgreSQL database integration
  • 🤖 Multi-provider AI model support
  • 📡 Flexible communication transport
  • 🛡️ Extensible tool registration

Supported AI Providers

  • Anthropic (Claude models)
  • Google (Gemini models)

Tools and Endpoints

Available Tools

  • list_tables(): Retrieve database tables
  • Custom tools can be easily added via decorators

Endpoints

  • /sse: Server-Sent Events endpoint
  • Customizable routing and tool registration

Extending the Framework

Adding New Tools

@app.tool()
def custom_tool():
    """Custom tool implementation"""
    # Your tool logic here

Configuring AI Providers

Modify lm_config.py to add or configure new AI providers.