MCP PostgreSQL Demo

by Tibiritabara

7 stars
197 downloads
Not rated
GitHub

About

A FastMCP server that enables LLMs to connect and interact with PostgreSQL databases. It demonstrates how to use the Model Context Protocol (MCP) to allow Language Models to query and explore database schemas and tables.

Details

Author
Tibiritabara
GitHub stars
7
Downloads
197
Categories
Database

- Schema Exploration: Retrieve metadata about database schemas
- Table Inspection: Get detailed information about table structures
- Database Querying: Execute SQL SELECT queries against the database
- YAML Formatting: Results returned in YAML format for LLM consumption
- Predefined prompts for schema, table descriptions, and data queries

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

  1. Download and install Highlight from highlightai.com/download
  2. Navigate to the plugins tab and select "Add Custom Plugin"
  3. Configure the plugin with the settings below
    Plugin Name MCP PostgreSQL Demo
    Command (node, npx, python, etc.)

    Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.

  4. Enable "Start Automatically" if you want the plugin to start when Highlight launches

From the repository

Install Python 3.12+, PostgreSQL, and UV. Clone the repository, create a virtual environment, install dependencies with uv sync, and configure environment variables. Uncomment the run function in src/main.py and start the server with python -m src.main. Alternatively, use Docker with the provided Dockerfile. For client applications, add the MCP configuration to the client’s MCP config file (e.g., .cursor/mcp.json).

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "mcp postgresql demo": {
            "postgres-mcp-tibiritabara": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    ".venv"
                ]
            }
        }
    }
}

McpServers

{
    "postgres-mcp-tibiritabara": {
        "command": "python",
        "args": [
            "-m",
            "venv",
            ".venv"
        ]
    }
}

MCP PostgreSQL Demo

A FastMCP server that enables LLMs to connect and interact with PostgreSQL databases. This project demonstrates how to use the Model Context Protocol (MCP) to allow Language Models to query and explore database schemas and tables.

Features

- Schema Exploration: Retrieve metadata about database schemas
- Table Inspection: Get detailed information about table structures
- Database Querying: Execute SQL queries against the database
- YAML Formatting: Results are returned in YAML format for easy consumption by LLMs

Resources

The server exposes the following MCP resources:

- database://{schema} - Get information about all tables in a schema
- database://{schema}/tables/{table} - Get detailed information about a specific table

Tools

- query_database - Execute SQL queries against the database (SELECT queries only)

Prompts

The server includes the following predefined prompts:

- prompt_schema_description - Ask for a description of a database schema
- prompt_table_description - Ask for a description of a specific table
- prompt_query_database - Ask for data from a specific table

Prerequisites

- Python 3.12 or higher
- PostgreSQL database
- UV package manager (recommended)

Installation

1. Clone the repository:

   git clone <repository-url>
   cd mcp-demo
   

2. Create a virtual environment:

   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   

3. Install UV (if not already installed):

   pip install uv
   

4. Install dependencies with UV:

   uv sync
   

5. Configure environment variables:
- Copy .env.example to .env
- Update the values according to your PostgreSQL configuration

Configuration

The application is configured using environment variables:

| Variable | Description | Default |
| ----------- | ------------------------ | --------- |
| APP_NAME | Application name | mcp-demo |
| DB_HOST | PostgreSQL host | localhost |
| DB_PORT | PostgreSQL port | 5432 |
| DB_USER | PostgreSQL username | postgres |
| DB_PASSWORD | PostgreSQL password | postgres |
| DB_NAME | PostgreSQL database name | postgres |

Usage

1. First, uncomment the run function in src/main.py by removing the comment from these lines at the bottom of the file:

   # if __name__ == "__main__":
   #     print("Starting FastMCP server...")
   #     mcp.run()
   

2. Start the FastMCP server:

   python -m src.main
   

3. The server will be available for LLMs to connect to and query your PostgreSQL database. With the server running, the MCP can be loaded into client applications for interaction.

Client Configuration

To use this MCP in a client application, add the following configuration to your client's MCP configuration file (e.g., .cursor/mcp.json):

{
  "mcpServers": {
    "postgres-mcp-server": {
      "command": "/path/to/your/venv/bin/mcp",
      "args": ["run", "/path/to/your/postgres-mcp/src/main.py"],
      "env": {
        "APP_NAME": "mcp-demo",
        "DB_HOST": "localhost",
        "DB_PORT": "5432",
        "DB_USER": "postgres",
        "DB_PASSWORD": "postgres",
        "DB_NAME": "postgres"
      }
    }
  }
}

Be sure to replace the paths with the actual paths to your virtual environment and project directory, and update the environment variables to match your PostgreSQL configuration.

Development

Install development dependencies with UV:

uv pip install -e ".[dev]"

Development tools included:

- JupyterLab for notebooks
- Pyright for type checking
- Ruff for linting

Docker

To run the application with Docker:

1. Build the Docker image:

   docker build -t mcp-demo .
   

2. Run the container:

   docker run --env-file .env.docker -p 8000:8000 mcp-demo

Example Usage

Get Schema Information

from mcp.client import get_client

client = get_client("http://localhost:8000")
schema_info = client.get_resource("database://public")
print(schema_info)

Get Table Details

table_info = client.get_resource("database://public/tables/users")
print(table_info)

Execute a Query

result = client.invoke_tool("query_database", {"query": "SELECT * FROM users LIMIT 10"})
print(result)

License

[Add your license information here]

Contributors

- Ricardo Santos <ricardo.santos.diaz@gmail.com>

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