Document Hugging Face MCP Server integration with MCP Gateway

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Overview

Document how to integrate the official Hugging Face MCP Server with MCP Gateway for AI/ML model access and software development workflows.

Server Details

• Provider: Hugging Face
• Category: Software Development / AI/ML
• Endpoint: https://hf.co/mcp
• Authentication: Open (no authentication required)
• Protocol: Standard MCP over HTTPS

Documentation Requirements

File Location

Create: docs/docs/using/servers/huggingface/huggingface-mcp.md

Content Structure

1. Overview

   • Introduction to Hugging Face MCP Server
   • AI/ML model access and inference capabilities
   • Integration with Hugging Face Hub and Spaces

2. Prerequisites

   • Optional Hugging Face account for enhanced features
   • API token for private models and increased rate limits
   • Understanding of model types and capabilities

3. Authentication Setup

   • Open access configuration (no auth required)
   • Optional API token setup for enhanced access
   • Private model access configuration
   • Rate limiting considerations

4. MCP Gateway Integration

   • Server registration in MCP Gateway
   • HTTPS endpoint configuration
   • Optional authentication middleware
   • Caching strategies for model responses

5. Available Tools

   • Model inference and generation tools
   • Dataset access and processing
   • Model information and metadata
   • Hugging Face Spaces integration
   • Pipeline and transformer operations

6. Usage Examples

   • Text generation with language models
   • Image processing with vision models
   • Audio processing with speech models
   • Model comparison and benchmarking
   • Dataset exploration and analysis
   • Custom model deployment and inference

7. Best Practices

   • Efficient model usage patterns
   • Caching strategies for performance
   • Rate limiting and quota management
   • Model selection guidelines
   • Cost optimization for inference

8. Troubleshooting

   • Model loading and inference errors
   • Rate limiting and quota issues
   • Model compatibility problems
   • Performance optimization tips

Configuration Examples

# MCP Gateway server configuration (open access)
servers:
  - id: "huggingface-official"
    name: "Hugging Face MCP Server"
    description: "Official Hugging Face AI/ML model access tools"
    transport:
      type: "https"
      endpoint: "https://hf.co/mcp"
      auth:
        type: "none"  # Open access
    settings:
      timeout: 180  # Longer timeout for model inference
      retry_attempts: 2
      rate_limit_handling: true
      cache_responses: true
      cache_ttl: 3600

# Enhanced access with API token
servers:
  - id: "huggingface-enhanced"
    name: "Hugging Face MCP Server (Enhanced)"
    transport:
      type: "https"
      endpoint: "https://hf.co/mcp"
      auth:
        type: "bearer"
        token: "${HUGGINGFACE_API_TOKEN}"
      headers:
        "User-Agent": "MCP-Gateway/0.8.0"
    settings:
      timeout: 300
      rate_limit_handling: true
      cache_responses: true
      private_models: true

Navigation Updates

Create docs/docs/using/servers/huggingface/.pages with:

title: Hugging Face
nav:
  - huggingface-mcp.md

Update main servers .pages to include Hugging Face section.

References

• Hugging Face MCP Server
• Hugging Face Hub Documentation
• Hugging Face API Documentation
• Hugging Face Transformers

Acceptance Criteria

• Comprehensive documentation file created
• Open access and API token authentication documented
• HTTPS endpoint integration guide provided
• Available Hugging Face tools and capabilities documented
• Configuration examples for both open and enhanced access
• Model inference and processing examples included
• Performance optimization and caching strategies documented
• Rate limiting and quota considerations covered
• Troubleshooting section with common issues
• Navigation structure updated
• Cross-references to Hugging Face documentation

Priority

High - Hugging Face is a critical AI/ML platform integration

Use Cases

• AI/ML model experimentation and development
• Text, image, and audio processing workflows
• Model comparison and evaluation
• Dataset exploration and analysis
• Custom AI application development
• Research and prototyping
• Educational AI/ML projects