HuggingMCP

HuggingMCP - Advanced Hugging Face MCP Server

A comprehensive and powerful Model Context Protocol (MCP) server for Hugging Face Hub operations, featuring 18+ specialized tools, AI workflow automation, and extensive ML capabilities.

🚀 Features

Core Capabilities

Optimized Command Structure: 18+ specialized commands covering all aspects of ML workflows
Enhanced Debugging: Comprehensive stderr output and logging for troubleshooting
Robust Error Handling: Safe execution wrappers with detailed error reporting and helpful guidance
Backward Compatibility: All existing commands maintained with enhanced functionality

New Advanced Features

🔬 Model Evaluation & Testing: Comprehensive model analysis, validation, and comparison tools
🗃️ Dataset Processing: Advanced dataset analysis, validation, and management
📝 License Management: Automated license checking, compliance validation, and suggestions
🤝 Community Features: Repository likes, discussions, commit history, and social interactions
🚀 Space Management: Complete Hugging Face Spaces control and monitoring
🧠 AI Inference Tools: Model testing and inference capabilities with multiple strategies
⚙️ Workflow Automation: Automated model card generation, README creation, and bulk operations
📊 Advanced Analytics: Trending analysis, recommendation engine, and ecosystem insights
🛠️ Repository Utilities: Health checks, backup tools, and comprehensive repository management

Enhanced File Operations

Batch Processing: Edit multiple files with pattern matching
File Validation: Format-specific validation for JSON, Markdown, Python, etc.
Backup System: Automatic backup creation before destructive operations
Advanced Reading: Chunked reading, encoding detection, and size management

📋 Prerequisites

Python 3.8+
Required packages:
pip install mcp huggingface_hub
Hugging Face token (set as HF_TOKEN environment variable)

⚙️ Configuration

Add to your Claude Desktop configuration file at: /Users/[username]/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "huggingmcp": {
      "command": "python3",
      "args": ["/Users/sshpro/Documents/hugmcp.py"],
      "env": {
        "HF_TOKEN": "your_hugging_face_token_here",
        "HF_ADMIN_MODE": "true",
        "HF_READ_ONLY": "false",
        "HF_WRITE_ONLY": "false",
        "HF_MAX_FILE_SIZE": "100000000"
      }
    }
  }
}

Environment Variables

HF_TOKEN: Your Hugging Face API token (required for write operations)
HF_ADMIN_MODE: Enable admin operations like repository deletion (default: false)
HF_READ_ONLY: Restrict to read-only operations (default: false)
HF_MAX_FILE_SIZE: Maximum file size for operations in bytes (default: 104857600 = 100MB)
HF_ENABLE_INFERENCE: Enable inference API features (default: true)
HF_INFERENCE_TIMEOUT: Timeout for inference operations in seconds (default: 30)
HF_CACHE_ENABLED: Enable caching for better performance (default: true)

🛠️ Available Commands

Core Commands (Enhanced)

1. `hf_system_info()`

Get comprehensive system information, configuration, and test connectivity.

# Returns server status, configuration, user info, capabilities, and new features

2. `hf_repository_manager(action, repo_id, repo_type="model", **kwargs)`

Comprehensive repository management.

Actions:

create: Create new repository
- private: Make repository private (default: False)
- description: Repository description
- space_sdk: For Spaces - "gradio", "streamlit", "docker", "static"
- creator: Repository creator (defaults to authenticated user)
delete: Delete repository (requires admin mode)
info: Get repository information
list_files: List all files in repository

Examples:

# Create a public model repository
hf_repository_manager("create", "my-awesome-model", "model", 
                     description="My awesome AI model")

# Create a private Gradio space
hf_repository_manager("create", "my-space", "space", 
                     private=True, space_sdk="gradio")

# Get repository info
hf_repository_manager("info", "microsoft/DialoGPT-medium")

# List files in repository
hf_repository_manager("list_files", "gpt2")

3. `hf_file_operations(action, repo_id, filename, repo_type="model", **kwargs)`

Enhanced file operations with advanced capabilities.

Actions:

read: Read file content with encoding detection
- max_size: Maximum characters to read (default: 500,000)
- chunk_size: Enable chunked reading
- chunk_number: Chunk number to read (for chunked reading)
write: Write/upload file content with validation
- content: File content to write
- commit_message: Commit message
edit: Edit file by replacing text with backup
- old_text: Text to replace
- new_text: Replacement text
- commit_message: Commit message
delete: Delete file from repository
validate: Validate file format and content
backup: Create backup of file before operations
batch_edit: Edit multiple files with pattern matching
- pattern: Text pattern to replace
- replacement: Replacement text
- file_patterns: File patterns to match (e.g., [".md", ".txt"])

Examples:

# Read a file (truncated to 1000 chars)
hf_file_operations("read", "gpt2", "README.md", max_size=1000)

# Read file in chunks
hf_file_operations("read", "gpt2", "config.json", chunk_size=1000, chunk_number=0)

# Write a new file
hf_file_operations("write", "my-repo", "new_file.txt", 
                  content="Hello World!", 
                  commit_message="Add new file")

# Edit existing file
hf_file_operations("edit", "my-repo", "README.md",
                  old_text="# Old Title",
                  new_text="# New Title",
                  commit_message="Update title")

4. `hf_search_hub(content_type, query=None, author=None, filter_tag=None, limit=20)`

Search Hugging Face Hub for models, datasets, or spaces.

Examples:

# Search for transformer models
hf_search_hub("models", query="transformer", limit=10)

# Search for datasets by specific author
hf_search_hub("datasets", author="huggingface", limit=5)

# Search for Gradio spaces
hf_search_hub("spaces", filter_tag="gradio")

5. `hf_collections(action, **kwargs)`

Manage Hugging Face Collections.

Actions:

create: Create new collection
- title: Collection title (required)
- namespace: Collection namespace (defaults to user)
- description: Collection description
- private: Make collection private
add_item: Add item to collection
- collection_slug: Collection identifier
- item_id: Item to add (repo ID)
- item_type: Type of item ("model", "dataset", "space")
- note: Optional note about the item
info: Get collection information
- collection_slug: Collection identifier

Examples:

# Create a new collection
hf_collections("create", title="My AI Models", 
               description="Collection of my favorite models")

# Add item to collection
hf_collections("add_item", 
               collection_slug="my-collection",
               item_id="gpt2", 
               item_type="model",
               note="Great base model")

6. `hf_pull_requests(action, repo_id, repo_type="model", **kwargs)`

Manage Pull Requests.

Actions:

create: Create empty PR
- title: PR title (required, min 3 characters)
- description: PR description
list: List PRs
- status: Filter by status ("open", "closed", "all")
- author: Filter by author
details: Get PR details
- pr_number: PR number to get details for
create_with_files: Create PR with file changes
- files: List of {path, content} dictionaries
- commit_message: Commit message
- pr_title: PR title
- pr_description: PR description

Examples:

# Create a simple PR
hf_pull_requests("create", "my-repo", title="Update documentation")

# Create PR with file changes
hf_pull_requests("create_with_files", "my-repo",
                 files=[{"path": "README.md", "content": "Updated content"}],
                 commit_message="Update README",
                 pr_title="Documentation update")

# List open PRs
hf_pull_requests("list", "my-repo", status="open")

7. `hf_upload_manager(action, repo_id, repo_type="model", **kwargs)`

Upload management with various options.

Actions:

single_file: Upload one file
- file_path: Path in repository
- content: File content
- commit_message: Commit message
multiple_files: Upload multiple files
- files: List of {path, content} dictionaries
- commit_message: Commit message
with_pr: Upload file(s) and create PR
- file_path: Path in repository
- content: File content
- commit_message: Commit message
- pr_title: PR title
- pr_description: PR description

8. `hf_batch_operations(operation_type, operations)`

Execute multiple operations in batch.

Operation Types:

search: Batch search operations
info: Batch repository info retrieval
files: Batch file listing

Example:

# Batch search multiple content types
hf_batch_operations("search", [
    {"content_type": "models", "query": "transformer", "limit": 5},
    {"content_type": "datasets", "query": "text", "limit": 3}
])

9. `hf_advanced_search(query, search_types=["models", "datasets", "spaces"], filters=None, limit_per_type=10)`

Advanced search across multiple content types with filtering and popularity scoring.

Example:

# Advanced search with filtering
hf_advanced_search("transformer", 
                   search_types=["models", "datasets"],
                   filters={"author": "huggingface"},
                   limit_per_type=15)

10. `hf_debug_diagnostics()`

Comprehensive debugging and diagnostic information.

# Get system diagnostics, connectivity tests, and debug info

11. `hf_repo_file_manager(action, repo_id, repo_type="model", filename=None, **kwargs)`

Unified repository and file management with rename support.

Actions:

repo_create, repo_delete, repo_info, list_files
file_read, file_write, file_edit, file_delete, file_rename

Example:

# Rename a file in a repository
hf_repo_file_manager("file_rename", "my-repo", filename="old.txt",
                     new_filename="new.txt", commit_message="Rename file")

New Advanced Commands

12. `hf_model_evaluation(action, repo_id, **kwargs)`

Advanced model evaluation and testing capabilities.

Actions:

analyze: Comprehensive model analysis including architecture, frameworks, and compatibility
compare: Compare multiple models side by side
test_inference: Test model inference capabilities (if supported)
validate_model: Validate model integrity and completeness

Examples:

# Analyze a model
hf_model_evaluation("analyze", "microsoft/DialoGPT-medium")

# Compare multiple models
hf_model_evaluation("compare", "gpt2", models=["gpt2", "distilgpt2", "microsoft/DialoGPT-medium"])

# Validate model
hf_model_evaluation("validate_model", "my-model")

13. `hf_space_management(action, space_id, **kwargs)`

Advanced Hugging Face Spaces management.

Actions:

runtime_info: Get space runtime information and status
restart: Restart a space
pause: Pause a space
set_sleep_time: Set sleep time for a space
duplicate: Duplicate a space to a new location

Examples:

# Get space runtime info
hf_space_management("runtime_info", "gradio/chatbot")

# Restart a space
hf_space_management("restart", "my-space")

# Duplicate a space
hf_space_management("duplicate", "original-space", to_id="my-copied-space")

14. `hf_community_features(action, repo_id, repo_type="model", **kwargs)`

Community features and social interactions.

Actions:

like: Like a repository
unlike: Unlike a repository
get_likes: Get user's liked repositories
create_discussion: Create a discussion (non-PR)
get_commits: Get repository commit history
get_refs: Get repository branches and tags

Examples:

# Like a repository
hf_community_features("like", "microsoft/DialoGPT-medium")

# Get commit history
hf_community_features("get_commits", "my-repo")

# Create a discussion
hf_community_features("create_discussion", "my-repo", 
                      title="Question about model", 
                      description="How do I use this model?")

15. `hf_dataset_processing(action, dataset_id, **kwargs)`

Advanced dataset processing and analysis tools.

Actions:

analyze: Analyze dataset structure, size, and metadata
compare: Compare multiple datasets
validate: Validate dataset format and completeness

Examples:

# Analyze a dataset
hf_dataset_processing("analyze", "squad")

# Compare datasets
hf_dataset_processing("compare", "squad", datasets=["squad", "glue", "imdb"])

# Validate dataset
hf_dataset_processing("validate", "my-dataset")

16. `hf_license_management(action, repo_id, repo_type="model", **kwargs)`

License management and compliance tools.

Actions:

check_license: Check repository license information
validate_compliance: Validate license compliance with scoring
suggest_license: Suggest appropriate license based on content type and preferences

Examples:

# Check license
hf_license_management("check_license", "my-model")

# Validate compliance
hf_license_management("validate_compliance", "my-model")

# Get license suggestions
hf_license_management("suggest_license", "my-model", 
                     content_type="model", commercial_use=True)

17. `hf_inference_tools(action, repo_id, **kwargs)`

Advanced inference and model testing tools.

Actions:

test_inference: Test model inference with custom inputs
check_endpoints: Check available inference endpoints

Examples:

# Test inference
hf_inference_tools("test_inference", "gpt2", 
                  inputs=["Hello world", "How are you?"],
                  parameters={"max_length": 50})

# Check endpoints
hf_inference_tools("check_endpoints", "my-model")

18. `hf_ai_workflow_tools(action, **kwargs)`

Specialized AI workflow and automation tools.

Actions:

create_model_card: Generate comprehensive model cards
bulk_operations: Perform bulk operations across repositories
generate_readme: Generate README files for repositories
validate_pipeline: Validate complete ML pipelines

Examples:

# Generate model card
hf_ai_workflow_tools("create_model_card", repo_id="my-model",
                    model_type="text-generation", language=["en"])

# Bulk operations
hf_ai_workflow_tools("bulk_operations", 
                    repo_list=["model1", "model2"], 
                    operation="validate")

# Generate README
hf_ai_workflow_tools("generate_readme", repo_id="my-model", repo_type="model")

19. `hf_advanced_analytics(action, **kwargs)`

Advanced analytics and insights for HuggingFace repositories.

Actions:

trending_analysis: Analyze trending models/datasets with metrics
recommendation_engine: Recommend repositories based on preferences

Examples:

# Trending analysis
hf_advanced_analytics("trending_analysis", content_type="models", limit=50)

# Get recommendations
hf_advanced_analytics("recommendation_engine", 
                     content_type="models",
                     preferences={"tags": ["transformers", "pytorch"], "min_downloads": 1000})

20. `hf_repository_utilities(action, repo_id, repo_type="model", **kwargs)`

Advanced repository utilities and management tools.

Actions:

repository_health: Comprehensive repository health check with scoring
backup_info: Create comprehensive backup information

Examples:

# Health check
hf_repository_utilities("repository_health", "my-model")

# Create backup info
hf_repository_utilities("backup_info", "my-model")

🔧 Debugging

The server includes comprehensive debugging features:

Stderr Output: Real-time debugging information printed to stderr
Log Files: Detailed logs written to /tmp/hugmcp_debug.log
Diagnostic Tools: Use hf_debug_diagnostics() for system health checks
Error Tracking: Comprehensive error handling with stack traces

Debug Log Locations

MCP Logs: /Users/[username]/Library/Logs/Claude/mcp-server-huggingmcp.log
Debug Logs: /tmp/hugmcp_debug.log

🛡️ Security & Permissions

Authentication: Requires HF_TOKEN for write operations
Read-Only Mode: Set HF_READ_ONLY=true to prevent modifications
Admin Mode: Set HF_ADMIN_MODE=true to enable repository deletion
File Size Limits: Configurable via HF_MAX_FILE_SIZE

🐛 Troubleshooting

Common Issues

Server Disconnects Immediately
- Check if the script path in configuration is correct
- Verify Python dependencies are installed
- Check debug logs for detailed error information
Authentication Errors
- Ensure HF_TOKEN is set correctly
- Verify token has required permissions
- Check token validity at huggingface.co
File Path Errors
- Verify the script is at /Users/sshpro/Documents/hugmcp.py
- Check file permissions (should be readable/executable)
Import Errors
- Install required packages: pip install mcp huggingface_hub
- Check Python version compatibility

Getting Help

Use hf_debug_diagnostics() for system information
Check stderr output for real-time debugging
Review log files for detailed error traces
Verify configuration in Claude Desktop settings

📝 Version History

v3.0.0 (Current) - Major Feature Release

Massive expansion: Added 9+ new advanced command categories (18+ total tools)
🔬 Model Evaluation: Complete model analysis, comparison, and validation system
🗃️ Dataset Processing: Advanced dataset analysis and validation tools
📝 License Management: Automated license checking and compliance validation
🤝 Community Features: Repository likes, discussions, commit history, social interactions
🚀 Space Management: Complete Hugging Face Spaces control and monitoring
🧠 AI Inference Tools: Model testing and inference capabilities
⚙️ Workflow Automation: Model card generation, README creation, bulk operations
📊 Advanced Analytics: Trending analysis, recommendation engine, ecosystem insights
🛠️ Repository Utilities: Health checks, backup tools, comprehensive management
Enhanced File Operations: Batch editing, validation, backup system, format detection
Improved Error Handling: Detailed validation, helpful error messages, guided troubleshooting
Graceful Degradation: Feature detection for different huggingface_hub versions
Backward Compatibility: All existing v2.x commands maintained and enhanced

v2.1.0

Consolidated 23+ commands into 11 optimized commands
Added unified repo/file manager with rename support
Enhanced debugging and error handling
Added batch operations and advanced search
Improved file operations with chunked reading
Added comprehensive diagnostics
Fixed metadata and connection stability issues

📄 License

This project is licensed under the MIT License.

🤝 Contributing

Contributions are welcome! Please ensure all changes maintain the current command structure and include appropriate error handling and debugging output.

This server cannot be installed

security - not tested

license - permissive license

quality - not tested

How are these scores calculated?

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

A Model Context Protocol server that allows Claude and other MCP-compatible AI assistants to interact with the Hugging Face ecosystem, enabling repository management, file operations, search, and collections management through natural language.

Related MCP Servers

Aider MCP Server
sengokudaikon
-
security
A
license
-
quality
A Model Context Protocol server that connects Claude and other MCP clients to Aider, enabling AI assistants to efficiently edit files, create new files, and interact with git repositories through natural language.
Last updated -
9
Python
The Unlicense
Hass-MCP
voska
-
security
A
license
-
quality
A Model Context Protocol server that enables AI assistants like Claude to interact directly with Home Assistant, allowing them to query device states, control smart home entities, and perform automation tasks.
Last updated -
104
Python
MIT License
Hugging Face MCP Server
shreyaskarnik
-
security
A
license
-
quality
A Model Context Protocol server that provides Claude and other LLMs with read-only access to Hugging Face Hub APIs, enabling interaction with models, datasets, spaces, papers, and collections through natural language.
Last updated -
4
Python
MIT License
Scenario Word
HyunJuHwan
A
security
F
license
A
quality
A Model Context Protocol (MCP) server that allows Claude AI to interact with custom tools, enabling extension of Claude's capabilities through the MCP framework.
Last updated -
TypeScript

View all related MCP servers

HuggingMCP - Advanced Hugging Face MCP Server

🚀 Features

Core Capabilities

New Advanced Features

Enhanced File Operations

📋 Prerequisites

⚙️ Configuration

Environment Variables

🛠️ Available Commands

Core Commands (Enhanced)

1. hf_system_info()

2. hf_repository_manager(action, repo_id, repo_type="model", **kwargs)

3. hf_file_operations(action, repo_id, filename, repo_type="model", **kwargs)

4. hf_search_hub(content_type, query=None, author=None, filter_tag=None, limit=20)

5. hf_collections(action, **kwargs)

6. hf_pull_requests(action, repo_id, repo_type="model", **kwargs)

7. hf_upload_manager(action, repo_id, repo_type="model", **kwargs)

8. hf_batch_operations(operation_type, operations)

9. hf_advanced_search(query, search_types=["models", "datasets", "spaces"], filters=None, limit_per_type=10)

10. hf_debug_diagnostics()

11. hf_repo_file_manager(action, repo_id, repo_type="model", filename=None, **kwargs)

New Advanced Commands

12. hf_model_evaluation(action, repo_id, **kwargs)

13. hf_space_management(action, space_id, **kwargs)

14. hf_community_features(action, repo_id, repo_type="model", **kwargs)

15. hf_dataset_processing(action, dataset_id, **kwargs)

16. hf_license_management(action, repo_id, repo_type="model", **kwargs)

17. hf_inference_tools(action, repo_id, **kwargs)

18. hf_ai_workflow_tools(action, **kwargs)

19. hf_advanced_analytics(action, **kwargs)

20. hf_repository_utilities(action, repo_id, repo_type="model", **kwargs)

🔧 Debugging

Debug Log Locations

🛡️ Security & Permissions

🐛 Troubleshooting

Common Issues

Getting Help

📝 Version History

v3.0.0 (Current) - Major Feature Release

v2.1.0

📄 License

🤝 Contributing

Related MCP Servers

Aider MCP Server

Hass-MCP

Hugging Face MCP Server

Scenario Word

New MCP Servers

MCP directory API

1. `hf_system_info()`

2. `hf_repository_manager(action, repo_id, repo_type="model", **kwargs)`

3. `hf_file_operations(action, repo_id, filename, repo_type="model", **kwargs)`

4. `hf_search_hub(content_type, query=None, author=None, filter_tag=None, limit=20)`

5. `hf_collections(action, **kwargs)`

6. `hf_pull_requests(action, repo_id, repo_type="model", **kwargs)`

7. `hf_upload_manager(action, repo_id, repo_type="model", **kwargs)`

8. `hf_batch_operations(operation_type, operations)`

9. `hf_advanced_search(query, search_types=["models", "datasets", "spaces"], filters=None, limit_per_type=10)`

10. `hf_debug_diagnostics()`

11. `hf_repo_file_manager(action, repo_id, repo_type="model", filename=None, **kwargs)`

12. `hf_model_evaluation(action, repo_id, **kwargs)`

13. `hf_space_management(action, space_id, **kwargs)`

14. `hf_community_features(action, repo_id, repo_type="model", **kwargs)`

15. `hf_dataset_processing(action, dataset_id, **kwargs)`

16. `hf_license_management(action, repo_id, repo_type="model", **kwargs)`

17. `hf_inference_tools(action, repo_id, **kwargs)`

18. `hf_ai_workflow_tools(action, **kwargs)`

19. `hf_advanced_analytics(action, **kwargs)`

20. `hf_repository_utilities(action, repo_id, repo_type="model", **kwargs)`