CSGHub MCP Servers provides a dedicated control surface for managing AI models, evaluation tools, finetuning, and dataset/code control within the CSGHub ecosystem. The service exposes an MCP API that lets you query model status, start/stop inference runs, trigger evaluations, manage spaces and datasets, and orchestrate training or finetuning pipelines through a unified interface. Use the included tools to integrate with CSGHub's toolchain, enabling automated workflows, model lifecycle management, and configurable evaluation scenarios. After starting the server, you can interact with the MCP endpoints to list available models, manage permissions, and invoke control operations for model inference, fine-tuning, and evaluation tasks as part of your deployment pipelines.

Prerequisites:

• Python 3.10 or higher
• Access to a CSGHub Server environment
• Optional: virtual environment tooling (venv) for isolation

Installation steps:

1. Clone the repository or download the MCP server package: git clone https://github.com/your-org/opencsgs-csghub-mcp-servers.git cd opencsgs-csghub-mcp-servers

2. Create and activate a virtual environment (recommended): python -m venv venv

   On Windows:

   venv\Scripts\activate

   On macOS/Linux:

   source venv/bin/activate

3. Install dependencies: pip install -r requirements.txt

4. Configure environment variables (example): export CSGHUB_API_KEY=your-csghub-api-key export CSGHUB_BASE_URL=https://api.csghub.example.org export LOG_LEVEL=INFO

5. Run the MCP server: python -m opencsgs_csghub_mcp_server

Notes:

• If you prefer a different execution method, you can adapt to docker or other runtimes if available, but this guide assumes the Python module approach.
• Ensure network access to the CSGHub API endpoints and that your API key has the necessary permissions.

Tips and considerations:

• The MCP server relies on the CSGHub API; store credentials securely (use environment variables or a secrets manager).
• Adjust LOG_LEVEL to control verbosity during development (DEBUG, INFO, WARNING, ERROR).
• If you integrate with automated pipelines, consider adding a watchdog or health-check endpoint to monitor the MCP server status.
• If you encounter module import errors, verify that the module name opencsgs_csghub_mcp_server matches your installed package layout. Ensure your PYTHONPATH includes the project or install in editable mode (pip install -e .).
• This MCP server is described as lightweight with extensible architecture; you can extend it by adding new control capabilities or integrating additional tools into the MCP API layer.