hf-mcp
Use Hugging Face Hub via MCP server tools. Search models, datasets, Spaces, papers. Get repo details, fetch documentation, run compute jobs, and use Gradio Spaces as AI tools. Available when connected to the HF MCP server.
Works with
---
name: hf-mcp
description: Use Hugging Face Hub via MCP server tools. Search models, datasets, Spaces, papers. Get repo details, fetch documentation, run compute jobs, and use Gradio Spaces as AI tools. Available when connected to the HF MCP server.
license: Apache-2.0
---
# 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": "$HF_TOKEN"}
})
```
### Check Job Status
```
User: "What's happening with my training job?"
1. hf_jobs(operation="ps")
2. hf_jobs(operation="logs", args={"job_id": "job-xxxxx"})
```
### Explore What's Trending
```
User: "What models are trending right now?"
model_search(sort="trendingScore", limit=20)
```
### Get Model Card Details
```
User: "Tell me about Mistral-7B"
hub_repo_details(repo_ids=["mistralai/Mistral-7B-v0.1"], include_readme=true)
```
### Find Quantized Models
```
User: "Find GGUF versions of Llama 3"
model_search(query="Llama 3 GGUF", sort="downloads", limit=10)
```
### Use a Gradio Space as a Tool
```
User: "Transcribe this audio file"
1. space_search(query="speech to text transcription", mcp=true)
2. dynamic_space(operation="view_parameters", space_name="openai/whisper")
3. dynamic_space(operation="invoke", space_name="openai/whisper", parameters="{\"audio\": \"...\"}")
```
### Schedule Recurring Jobs
```
User: "Run this data sync every day at midnight"
hf_jobs(operation="scheduled uv", args={
"script": "...",
"cron": "0 0 * * *",
"flavor": "cpu-basic"
})
```
## Tool Selection Guide
| Goal | Tool |
|------|------|
| Find models | `model_search` |
| Find datasets | `dataset_search` |
| Find Spaces/apps | `space_search` |
| Find papers | `paper_search` |
| Get repo README/details | `hub_repo_details` |
| Learn library usage | `hf_doc_search` → `hf_doc_fetch` |
| Run code on GPU/CPU | `hf_jobs` |
| Use Gradio apps as tools | `dynamic_space` |
| Generate images | `gr1_flux1_schnell_infer` or `dynamic_space` |
| Check auth | `hf_whoami` |
## Tips
- Use `sort="trendingScore"` to find what's popular now
- Use `sort="downloads"` to find battle-tested options
- Set `mcp=true` in `space_search` to find Spaces usable as tools
- Use `include_readme=true` in `hub_repo_details` for full model/dataset documentation
- For jobs accessing private repos, always include `secrets: {"HF_TOKEN": "$HF_TOKEN"}`
- Use `dynamic_space(operation="discover")` to see all available Space-based tasksMore Writing & Documentation skills
paper-context-resolver
lllllllama/rigorpilot-skills
Rigor Paper Context helper for README-first deep learning repo reproduction. Use only when the README and repository files leave a narrow reproduction-critical gap and the task is to resolve a specific paper detail such as dataset split, preprocessing, evaluation protocol, checkpoint mapping, or runtime assumption from primary paper sources while recording conflicts. Do not use for general paper summary, repo scanning, environment setup, command execution, title-only paper lookup, or replacing README guidance by default.
repo-intake-and-plan
lllllllama/rigorpilot-skills
Rigor Intake helper for README-first deep learning repo reproduction. Use when the task is specifically to scan a repository, read the README and common project files, extract documented commands, classify inference, evaluation, and training candidates, and return the smallest trustworthy reproduction plan to the main orchestrator. Do not use for environment setup, asset download, command execution, final reporting, paper lookup, or end-to-end orchestration.
minimal-run-and-audit
lllllllama/rigorpilot-skills
Rigor Run skill for README-first deep learning repo reproduction. Use when the task is specifically to capture or normalize evidence from the selected smoke test or documented inference or evaluation command and write standardized `repro_outputs/` files, including patch notes when repository files changed. Do not use for training execution, initial repo intake, generic environment setup, paper lookup, target selection, hidden scientific-meaning changes, or end-to-end orchestration by itself.

