π€ HuggingMCP - Hugging Face Model Context Protocol Server
About
Model Context Protocol (MCP) server for HuggingFace integration - lets Claude create, manage, and interact with HuggingFace spaces, models, datasets, and collections
Details
- Author
- ProCreations-Official
- Downloads
- 253
- Categories
- AI
Jump to
- Create, delete, and manage Hugging Face repositories
- Create and manage pull requests with file uploads
- Read, write, edit, and delete files on repositories
- Search models, datasets, and spaces with filters
- Organize repositories into collections
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
π€ HuggingMCP - Hugging Face Model Context Protocol ServerCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Install the server in a Python 3.10+ environment with uv or pip, then configure your Hugging Face token and permission settings in the Claude Desktop configuration file. After restarting Claude, you can use natural language commands to perform operations like creating repositories, editing files, searching models, and managing pull requests.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"\ud83e\udd17 huggingmcp - hugging face model context protocol server": {
"HuggingMCP": {
"command": "uv",
"args": [
"venv"
]
}
}
}
}McpServers
{
"HuggingMCP": {
"command": "uv",
"args": [
"venv"
]
}
}π€ HuggingMCP - Hugging Face Model Context Protocol Server
Give Claude superpowers with Hugging Face! π
HuggingMCP is a comprehensive Model Context Protocol (MCP) server that allows Claude and other MCP-compatible AI assistants to interact seamlessly with the Hugging Face ecosystem. Create models, manage datasets, edit files, organize collections, and much more - all through natural language!
β¨ Features
ποΈ Repository Management
- Create repositories (models, datasets, spaces) with custom settings - Delete repositories (admin mode required) - Get detailed repository information and metadata - List repository files and directory structuresπ Pull Requests & Collaboration
- Create pull requests on any public/private HuggingFace repository - Upload files with PR - make changes and create PRs in one step - Commit with PR - bundle multiple file changes into a single PR - List pull requests with filters (status, author, repo type) - Get PR details including comments, conflicts, and git references - Draft mode support - PRs start in draft for safe collaborationπ Advanced File Operations
- Read files from any Hugging Face repository (public/private) - Write/upload files with custom content - Precise file editing with exact text replacement (old_text β new_text) - Delete files from repositories - Binary file support for non-text filesπ Search & Discovery
- Search models with filters (author, tags, popularity) - Search datasets across all of Hugging Face - Search Spaces and demo applications - Advanced filtering by downloads, likes, creation date - Comprehensive metadata for all resultsπ Collections Management
- Create collections to organize repositories - Add items to collections (models, datasets, spaces, papers) - Manage collection metadata and descriptions - Get collection information and item listsπ Security & Permissions
- Token-based authentication with Hugging Face - Permission controls: read-only, write-only, admin modes - File size limits to prevent abuse - Comprehensive error handlingπ Quick Start
Prerequisites
- Python 3.10+ installed on your system
- Claude Desktop application (Download here)
- Hugging Face account and access token (Get token here)
Installation
1. Create a project directory:
mkdir huggingmcp && cd huggingmcp
2. Save the main.py file from the artifact in your project directory
3. Install dependencies:
```bash
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