# Hugging Face MCP server

Connect to Hugging Face Hub and thousands of Gradio AI Applications

## Links
- Registry page: https://www.getdrio.com/mcp/co-huggingface-hf-mcp-server

## Install
- Endpoint: https://huggingface.co/mcp
- Auth: Auth required by registry metadata

## Setup notes
- Remote header: Authorization (secret)
- The upstream registry signals required auth or secrets.
- Remote endpoint: https://huggingface.co/mcp?login
- Remote endpoint: https://huggingface.co/mcp
- Header: Authorization

## Tools
- hf_whoami (Hugging Face User Info) - Hugging Face tools are being used anonymously and may be rate limited. Call this tool for instructions on joining and authenticating. Endpoint: https://huggingface.co/mcp
- space_search (Hugging Face Space Search) - Find Hugging Face Spaces using semantic search. IMPORTANT Only MCP Servers can be used with the dynamic_space toolInclude links to the Space when presenting the results. Endpoint: https://huggingface.co/mcp
- hub_repo_search (Repo Search) - Search Hugging Face repositories with a shared query interface. You can target models, datasets, spaces, or aggregate across multiple repo types in one call. Use space_search for semantic-first discovery of Spaces. Include links to repositories in your response. Endpoint: https://huggingface.co/mcp
- paper_search (Paper Search) - Find Machine Learning research papers on the Hugging Face hub. Include 'Link to paper' When presenting the results. Consider whether tabulating results matches user intent. Endpoint: https://huggingface.co/mcp
- hub_repo_details - Get details for one or more Hugging Face repos (model, dataset, or space). Auto-detects type unless specified. For datasets, use operations: overview, dataset_structure, dataset_preview. Use dataset_structure first to discover configs, splits, sizes, and schema. Use dataset_preview only when config and split are known, unless the dataset has a single config/split. Endpoint: https://huggingface.co/mcp
- hf_fs (Hugging Face Files) - List or read files from a Hugging Face repo or bucket. Anonymous requests must include an owner. Endpoint: https://huggingface.co/mcp
- hf_doc_search (Hugging Face Documentation Search) - Search and Discover Hugging Face Product and Library documentation. Send an empty query to discover structure and navigation instructions. Knowledge up-to-date as at 3 July 2026. Combine with the Product filter to focus results. Endpoint: https://huggingface.co/mcp
- hf_doc_fetch (Fetch a document from the Hugging Face documentation library) - Fetch a document from the Hugging Face or Gradio documentation library. For large documents, use offset to get subsequent chunks. Endpoint: https://huggingface.co/mcp

## Resources
- skill://hf-cli/SKILL.md - Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. Use when: handling authentication; managing local cache; managing Hugging Face Buckets; running or scheduling jobs on Hugging Face infrastructure; managing Hugging Face repos; discussions and pull requests; browsing models, datasets and spaces; reading, searching, or browsing academic papers; managing collections; querying datasets; configuring spaces; setting up webhooks; or deploying and managing HF Inference Endpoints. Make sure to use this skill whenever the user mentions 'hf', 'huggingface', 'Hugging Face', 'huggingface-cli', or 'hugging face cli', or wants to do anything related to the Hugging Face ecosystem and to AI and ML in general. Also use for cloud storage needs like training checkpoints, data pipelines, or agent traces. Use even if the user doesn't explicitly ask for a CLI command. Replaces the deprecated `huggingface-cli`. MIME type: text/markdown
- skill://hf-mem/SKILL.md - Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub MIME type: text/markdown
- skill://huggingface-best/SKILL.md - Use when the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by benchmark scores. Triggers on: "best model for X", "what model should I use for", "top models for [task]", "which model runs on my laptop/machine/device", "recommend a model for", "what LLM should I use for", "compare models for", "what's state of the art for", or any question about choosing an AI model for a specific use case. Always use this skill when the user wants model recommendations or comparisons, even if they don't explicitly mention HuggingFace or benchmarks. MIME type: text/markdown
- skill://huggingface-community-evals.tar.gz - MIME type: application/gzip
- skill://huggingface-datasets/SKILL.md - Use this skill for Hugging Face Dataset Viewer API workflows that fetch subset/split metadata, paginate rows, search text, apply filters, download parquet URLs, and read size or statistics. MIME type: text/markdown
- skill://huggingface-gradio.tar.gz - MIME type: application/gzip
- skill://huggingface-llm-trainer.tar.gz - MIME type: application/gzip
- skill://huggingface-local-models.tar.gz - MIME type: application/gzip
- skill://huggingface-lora-space-builder.tar.gz - MIME type: application/gzip
- skill://huggingface-paper-publisher.tar.gz - MIME type: application/gzip
- skill://huggingface-papers/SKILL.md - Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata such as authors, linked models/datasets/spaces, Github repo and project page. Use when the user shares a Hugging Face paper page URL, an arXiv URL or ID, or asks to summarize, explain, or analyze an AI research paper. MIME type: text/markdown
- skill://huggingface-spaces.tar.gz - MIME type: application/gzip
- skill://huggingface-tool-builder.tar.gz - MIME type: application/gzip
- skill://huggingface-trackio.tar.gz - MIME type: application/gzip
- skill://huggingface-vision-trainer.tar.gz - MIME type: application/gzip
- skill://huggingface-zerogpu.tar.gz - MIME type: application/gzip
- skill://train-sentence-transformers.tar.gz - MIME type: application/gzip
- skill://transformers-js.tar.gz - MIME type: application/gzip
- skill://trl-training/SKILL.md - Train and fine-tune transformer language models using TRL (Transformers Reinforcement Learning). Supports SFT, DPO, GRPO, KTO, RLOO and Reward Model training via CLI commands. MIME type: text/markdown
- skill://index.json - Catalog of skills exposed by this server (SEP-2640 index). MIME type: application/json

## Prompts
- User Summary - Generate a summary of a Hugging Face user including their profile, models, datasets, spaces, and papers. Enter either a username (e.g., "clem") or a Hugging Face profile URL (e.g., "hf.co/julien-c" or "huggingface.co/thomwolf"). Arguments: user_id
- Paper Summary - Generate a comprehensive summary of an arXiv paper including its details and related models, datasets, and spaces on Hugging Face. Accepts various formats: "2502.16161", "arxiv:2502.16161", "https://arxiv.org/abs/2502.16161", or Hugging Face paper URLs. Arguments: paper_id
- Model Details - Get detailed information about a model from the Hugging Face Hub. Includes README from the repository - review before use. Arguments: model_id
- Dataset Details - Get detailed information about a dataset from the Hugging Face Hub. Includes README from the repository - review before use. Arguments: dataset_id

## Metadata
- Owner: co.huggingface
- Version: 0.2.33
- Runtime: Streamable Http
- Transports: HTTP
- License: Not captured
- Language: Not captured
- Stars: Not captured
- Updated: Oct 22, 2025
- Source: https://registry.modelcontextprotocol.io