MCP (Model Context Protocol) Server

by VajraM-dev

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# 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 ├──…

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

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:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below

Plugin Name MCP (Model Context Protocol) Server

Command (node, npx, python, etc.)

Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "mcp (model context protocol) server": {
            "Postgres-MCP-Server-With-SSE-Transport": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp (model context protocol) server": {
            "Postgres-MCP-Server-With-SSE-Transport": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp (model context protocol) server": {
            "Postgres-MCP-Server-With-SSE-Transport": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "mcp (model context protocol) server": {
            "Postgres-MCP-Server-With-SSE-Transport": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp (model context protocol) server": {
            "Postgres-MCP-Server-With-SSE-Transport": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp (model context protocol) server": {
            "Postgres-MCP-Server-With-SSE-Transport": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "mcp (model context protocol) server": {
            "Postgres-MCP-Server-With-SSE-Transport": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp (model context protocol) server": {
            "Postgres-MCP-Server-With-SSE-Transport": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp (model context protocol) server": {
            "Postgres-MCP-Server-With-SSE-Transport": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "mcp (model context protocol) server": {
            "Postgres-MCP-Server-With-SSE-Transport": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp (model context protocol) server": {
            "Postgres-MCP-Server-With-SSE-Transport": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp (model context protocol) server": {
            "Postgres-MCP-Server-With-SSE-Transport": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "mcp (model context protocol) server": {
            "Postgres-MCP-Server-With-SSE-Transport": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp (model context protocol) server": {
            "Postgres-MCP-Server-With-SSE-Transport": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp (model context protocol) server": {
            "Postgres-MCP-Server-With-SSE-Transport": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

From the repository

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

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

pip install -r requirements.txt

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

- 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

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

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

Claude Desktop / Cursor

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

{
    "mcpServers": {
        "mcp (model context protocol) server": {
            "Postgres-MCP-Server-With-SSE-Transport": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

McpServers

{
    "Postgres-MCP-Server-With-SSE-Transport": {
        "command": "python",
        "args": [
            "-m",
            "venv",
            "venv"
        ]
    }
}

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.

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About

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Setting up with Highlight

From the repository

Claude Desktop / Cursor

McpServers

Project Structure

Prerequisites

Installation

1. Clone the Repository

2. Create Virtual Environment

3. Install Dependencies

4. Configure Environment

Configuration Parameters

Running the Server

Development Mode

Client Interaction

Key Features

Supported AI Providers

Tools and Endpoints

Available Tools

Endpoints

Extending the Framework

Adding New Tools

Configuring AI Providers

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