New to Claude Skills? Learn how to install them →

huggingface on GitHub

Hugging Face MCP Server

Free

Connect AI assistants to the Hugging Face Hub seamlessly.

Get this skill

Free · Opens the source repo

What Hugging Face MCP Server does

The Hugging Face MCP Server skill allows developers and researchers to leverage the extensive resources available on the Hugging Face Hub through their AI assistants. By connecting to the MCP server, users can perform a variety of tasks including searching for models, datasets, and research papers, as well as running compute jobs and utilizing Gradio Spaces as tools. This integration streamlines the process of finding and using AI resources, making it easier to implement machine learning solutions in various projects.

With this skill, users can search for the best models tailored to specific tasks, such as code generation or sentiment analysis. It provides functionality to compare models from different authors, ensuring users can make informed decisions based on metrics like downloads or trending scores. Additionally, the skill facilitates the discovery of datasets and papers, helping users stay updated with the latest advancements in AI research.

The skill also supports practical applications, such as running scripts on cloud GPUs and scheduling recurring jobs. This is particularly useful for developers looking to automate tasks or run intensive computations without needing to manage the underlying infrastructure. The ability to interact with Gradio Spaces allows users to utilize pre-built AI tools for various applications, enhancing productivity and creativity.

Overall, the Hugging Face MCP Server skill is designed for AI developers, data scientists, and researchers who need efficient access to machine learning resources and tools. By simplifying the interaction with the Hugging Face Hub, it empowers users to focus on building and deploying AI applications more effectively.

When to use it

Use this skill when you need to search for models, datasets, or papers on the Hugging Face Hub, or when you want to run compute jobs on cloud resources.

When not to use it

This skill may not be suitable for users who do not require integration with the Hugging Face Hub or those who prefer local development environments without cloud dependencies.

What you can build with it

Searching for AI Models

Quickly find the best models for specific tasks like text generation or image classification using the model_search function.

Running Compute Jobs

Easily execute Python scripts on cloud GPUs with the hf_jobs function, allowing for scalable processing without local setup.

Exploring Research Papers

Stay updated on the latest developments in AI by searching for relevant papers using the paper_search function.

How to install Hugging Face MCP Server

View source

1. Install with the skills CLI

npx skills add huggingface/skills/hf-mcp --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by huggingface

Hugging Face MCP Server

Connect AI assistants to the Hugging Face Hub. Setup: https://huggingface.co/settings/mcp

Use Cases & Examples

Find the Best Model for a Task

User: "Find the best model for code generation"

1. model_search(task="text-generation", query="code", sort="trendingScore", limit=10)
2. hub_repo_details(repo_ids=["top-result-id"], include_readme=true)

Compare Models from Different Providers

User: "Compare Llama vs Qwen for text generation"

1. model_search(author="meta-llama", task="text-generation", sort="downloads", limit=5)
2. model_search(author="Qwen", task="text-generation", sort="downloads", limit=5)
3. hub_repo_details(repo_ids=["meta-llama/Llama-3.2-1B", "Qwen/Qwen3-8B"], include_readme=true)

Find Training Datasets

User: "Find datasets for sentiment analysis in English"

1. dataset_search(query="sentiment", tags=["language:en", "task_categories:text-classification"], sort="downloads")
2. hub_repo_details(repo_ids=["top-dataset-id"], repo_type="dataset", include_readme=true)

Discover AI Tools (MCP Spaces)

User: "Find a tool that can remove image backgrounds"

1. space_search(query="background removal", mcp=true)
2. dynamic_space(operation="view_parameters", space_name="result-space-id")
3. dynamic_space(operation="invoke", space_name="result-space-id", parameters="{...}")

Generate Images

User: "Create an image of a robot reading a book"

1. dynamic_space(operation="discover")  # See available tasks
2. gr1_flux1_schnell_infer(prompt="a robot sitting in a library reading a book, warm lighting, detailed")

Research a Topic

User: "What are the latest papers on RLHF?"

1. paper_search(query="reinforcement learning from human feedback", results_limit=10)
2. hub_repo_details(repo_ids=["paper-linked-model"], include_readme=true)  # If paper links to models

Learn How to Use a Library

User: "How do I fine-tune with LoRA using PEFT?"

1. hf_doc_search(query="LoRA fine-tuning", product="peft")
2. hf_doc_fetch(doc_url="https://huggingface.co/docs/peft/...")

Run a Quick GPU Job

User: "Run this Python script on a GPU"

hf_jobs(operation="uv", args={
  "script": "# /// script\n# dependencies = [\"torch\"]\n# ///\nimport torch\nprint(torch.cuda.is_available())",
  "flavor": "t4-small"
})

Train a Model on Cloud GPU

User: "Run my training script on an A10G"

hf_jobs(operation="run", args={
  "image": "pytorch/pytorch:2.5.1-cuda12.4-cudnn9-runtime",
  "command": ["/bin/sh", "-lc", "pip install transformers trl && python train.py"],
  "flavor": "a10g-small",
  "secrets": {"HF_TOKEN": "$HF_TOKEN"}
})

Check Job Status

User: "What's happening with my training job?"

1. hf_jobs(operation="ps")
2. hf_jobs(operation="logs", args={"job_id": "job-xxxxx"})

Explore What's Trending

User: "What models are trending right now?"

model_search(sort="trendingScore", limit=20)

Get Model Card Details

User: "Tell me about Mistral-7B"

hub_repo_details(repo_ids=["mistralai/Mistral-7B-v0.1"], include_readme=true)

Find Quantized Models

User: "Find GGUF versions of Llama 3"

model_search(query="Llama 3 GGUF", sort="downloads", limit=10)

Use a Gradio Space as a Tool

User: "Transcribe this audio file"

1. space_search(query="speech to text transcription", mcp=true)
2. dynamic_space(operation="view_parameters", space_name="openai/whisper")
3. dynamic_space(operation="invoke", space_name="openai/whisper", parameters="{\"audio\": \"...\"}")

Schedule Recurring Jobs

User: "Run this data sync every day at midnight"

hf_jobs(operation="scheduled uv", args={
  "script": "...",
  "cron": "0 0 * * *",
  "flavor": "cpu-basic"
})

Tool Selection Guide

GoalTool
Find modelsmodel_search
Find datasetsdataset_search
Find Spaces/appsspace_search
Find paperspaper_search
Get repo README/detailshub_repo_details
Learn library usagehf_doc_searchhf_doc_fetch
Run code on GPU/CPUhf_jobs
Use Gradio apps as toolsdynamic_space
Generate imagesgr1_flux1_schnell_infer or dynamic_space
Check authhf_whoami

Tips

  • Use sort="trendingScore" to find what's popular now
  • Use sort="downloads" to find battle-tested options
  • Set mcp=true in space_search to find Spaces usable as tools
  • Use include_readme=true in hub_repo_details for full model/dataset documentation
  • For jobs accessing private repos, always include secrets: {"HF_TOKEN": "$HF_TOKEN"}
  • Use dynamic_space(operation="discover") to see all available Space-based tasks

Frequently asked questions about Hugging Face MCP Server

Similar skills