hf-mcp

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• SKILL.md

SKILL.md (first lines)

# 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)
```

From huggingface/skills, Apache-2.0.

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