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Void MCP Server

Void MCP Server

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github.com/theadminautomatedMemory & Knowledge2Updated 73w ago

About Void MCP Server

Void MCP Server stores and retrieves LLM context data, with a PostgreSQL-backed MCP API and an optional local Hugging Face model and Streamlit interface. It is intended for teams connecting IDE context features to a locally run service.

Key features

  1. 01Stores and retrieves LLM context
  2. 02PostgreSQL configuration
  3. 03Starts MCP and model servers together
  4. 04Optional Streamlit interface

Use cases

  1. 01Connecting IDE context features to an MCP service
  2. 02Running a local model alongside context storage

MCP support

Exposes an MCP API for storing and retrieving LLM context data. The provided start script launches the MCP server alongside the local model server.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Void MCP Server

This project provides a Model Context Protocol (MCP) server for storing and retrieving LLM context data. It ships with run_local.py for running a lightweight Hugging Face model and app.py for a simple Streamlit UI.

Quick Start

  1. Install prerequisites

    • Node.js and npm
    • Python 3.11
    • PostgreSQL (ensure the service is running)
  2. Install dependencies

    npm install          # Node packages
    pip install -r requirements.txt
    
  3. Configure the database

    Create a .env file in the project root and set at least the following variables:

    DATABASE_HOST=localhost
    DATABASE_PORT=5432
    DATABASE_NAME=llm_context_db
    DATABASE_USER=your_db_user
    DATABASE_PASSWORD=your_db_password  # must be a string
    PORT=3000
    JWT_SECRET=change-me-in-production-min-32-chars
    

    If DATABASE_PASSWORD is missing you will see the error: SASL: SCRAM-SERVER-FIRST-MESSAGE: client password must be a string.

  4. Build the server

    npm run build
    
  5. Run everything

    python start.py
    

    The MCP API will be available at http://localhost:3000 and the local model server at http://localhost:8000. Press Ctrl+C to stop them.

  6. Optional: Launch the Streamlit interface

    streamlit run app.py
    

Development Tips

  • Run tests with pytest and npm test.

  • Start only the local model using python run_local.py --model sshleifer/tiny-gpt2 --port 8000.

  • Format and lint with pre-commit run --all-files.

  • setup.sh provides an interactive database setup if you need it.

  • Install dependencies

    • Node: npm install
    • Python: pip install -r requirements.txt
  • Build: npm run build

  • Tests: npm test for Node, pytest for Python

  • Run local model: python run_local.py --model sshleifer/tiny-gpt2 --port 8000

  • Streamlit UI: streamlit run app.py

One-Click Start

To launch both the MCP server and a local model server in one step, run:

python start.py

The start.py script is cross-platform and installs any missing Node and Python dependencies, builds the TypeScript server if necessary, and then starts the MCP server alongside the local model server. On Unix-like systems you can also use ./start.sh.

The script installs missing Node dependencies, builds the server if needed, and then starts the MCP service alongside the local model server. Connection details are printed to the terminal for easy integration with IDEs and tools.

IDE Integration

To connect your IDE's context features to the MCP server, provide a configuration file like ide_config.json pointing to start.py in this repository.

Replace /path/to/Void-MCP-Server/start.py with the absolute path on your system:

{
  "context_servers": {
    "my-mcp-server": {
      "command": {
        "path": "python",
        "args": [
          "/path/to/Void-MCP-Server/start.py"
        ],
        "env": {}
      },
      "settings": {}
    }
  }
}

Run python start.py --help for available options such as --model, --port, and --model-port to customize the servers.

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

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