Hugging Face Spaces

🚀 MCP Server Guide for HuggingFace Spaces

This guide provides a comprehensive overview of deploying and managing the MCP server on HuggingFace Spaces, including installation, configuration, usage, known issues, and third - party service information.

🚀 Quick Start

📦 Installation

To run the MCP server on HuggingFace Spaces, follow these steps:

1. Create a New Space:

   • Log in to HuggingFace Spaces.
   • Click “Create Space” and select “New from zeros”.
   • Name the space (e.g., mcp - hfspace).

2. Deploy the MCP Server:

   • Replace the existing code in the app.py file of the Space with the following:

from modelcontextprotocol.mcp import server

server.run(
    config={
        "spaces": {
            "llmindset/mcp-hfspace": {
                "command": "npx",
                "args": [
                    "-y",
                    "@llmindset/mcp-hfspace",
                    "--work-dir=~/mcp-files/",
                    "--HF_TOKEN=your_token_here"
                ],
                "models": [
                    "Qwen/Qwen2.5-72B-Instruct",
                    "black-forest-labs/FLUX.1-schnell",
                    "llmindset/mcp-hfspace/example/specific-endpoint"
                ]
            }
        }
    }
)

3. Set Environment Variables:

   • Add the HF_TOKEN environment variable and set it to your HuggingFace token.

4. Deploy the Space:

   • Click “Deploy” and wait for the Space to finish deploying.

💻 Running the MCP Server

• Open a terminal and navigate to the Space's folder.
• Enter the following command to run the server:

npx @llmindset/mcp-hfspace --work-dir=~/mcp-files/ --HF_TOKEN=your_token_here model1 model2 specific-endpoint...

Parameter Explanation

• --work-dir: Specify the working directory (default is the current directory).
• --HF_TOKEN: HuggingFace token for accessing private Spaces.

💻 Usage Examples

✨ Features of the MCP Server

The MCP server implements the Model Context Protocol (MCP) and supports the following features:

1. Model Integration:

   • Integrate multiple Hugging Face Spaces models.
   • Support tasks such as text generation and image processing.

2. Context Management:

   • Manage the context information during model runtime.
   • Provide rich tool descriptions and interactive interfaces.

3. Resource Management:

   • Monitor and manage server resources (CPU/GPU).
   • Support breakpoint resumption and task priority settings.

4. Logging and Status Monitoring:

   • View the server's running status in real - time.
   • Provide detailed execution logs and performance metrics.

Basic Usage

Text Generation

mcp-instruct Qwen/Qwen2.5-72B-Instruct --prompt "Tell a story about artificial intelligence"

Image Processing

mcp-vision microsoft/OmniParser --input image.jpg --task "Image description"

Multi - Model Integration

mcp-composite llmindset/mcp-hfspace \
    --model1 Qwen/Qwen2.5-72B-Instruct \
    --model2 black-forest-labs/FLUX.1-schnell \
    --prompt "Generate a creative advertising title"

Advanced Usage

The MCP provides the following tools to enhance functionality:

• Context Console: Manage the model's runtime context through an interactive interface.
• Remote Debugger: Monitor and adjust server parameters in real - time.
• Docking System: Support custom plugin development.

🔧 Technical Details

Known Issues and Limitations

Performance Limitations

• Insufficient Resources:
  • Insufficient CPU/GPU resources may cause the model to run slowly or fail.
• Memory Limitations:
  • Excessively large contexts or input data may cause memory overflow.

Functionality Limitations

• No Support for Real - Time Inference:
  • The current version does not support online inference.
• Limited Scalability:
  • The multi - model integration capabilities need further optimization.

Security Precautions

• Token Leakage Risk:
  • Ensure that the HF_TOKEN is not leaked during deployment.
• Network Attack Risk:
  • Uncontrolled functions may face potential security threats.

Third - Party Services

Advantages of Using Hugging Face Spaces

1. No Local Deployment Required:
   • Run the server in the cloud to avoid occupying local resources.
2. Automatic Scaling:
   • Hugging Face provides automatic scaling to handle traffic fluctuations.
3. Community Support:
   • Benefit from extensive open - source community support and a rich model library.

Optimization Suggestions

1. Regularly Update Models:
   • Use the latest version of models for better performance and security.
2. Monitor Running Status:
   • Deploy monitoring tools to detect and resolve issues promptly.
3. Optimize Resource Allocation:
   • Adjust resource allocation strategies according to actual needs.

With the above guide, you can successfully deploy and manage the MCP server on HuggingFace Spaces and leverage its powerful features to improve the efficiency of model development and application. For further assistance, refer to the HuggingFace documentation or contact community support.