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hf-mcp

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UpdatedJanuary 6, 2026 at 10:58

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.

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SKILL.md

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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.

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 tasks

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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`.

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Discover the user's local AWS context (active profile, region, account ID, caller identity) at the start of any AWS task. Use this skill before any other AWS work — deploying to SageMaker, creating resources, calling AWS APIs, or anything that touches an AWS account. Use it especially when the user has not specified a region or profile explicitly, when they say things like "use my AWS account", "deploy to AWS", "use my profile", or when about to make any AWS CLI or SDK call. Never guess the region or account ID — always use this skill to read it from the local configuration first.

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Set up an isolated Python environment for SageMaker / AWS work, with the right Python version and current boto3. Use this skill whenever Python code will be executed for a SageMaker deployment, training job, or any AWS automation — including when about to run `pip install`, when about to invoke `boto3`, when creating or activating a virtualenv, or when the user asks to "set up the environment". Never use system Python and never `pip install` into it. Always isolate. This skill prevents the most common failure modes: wrong Python version, dependency conflicts, and stale SDKs.

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Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host, serve, or expose a model on SageMaker or AWS — including phrases like "deploy a model", "host this LLM on AWS", "serve this embedding model", "deploy a reranker", "deploy a text-to-image / diffusion model", "host this for async inference", "create an endpoint", "serve my fine-tuned model", or any request that involves making a model available for inference on AWS. Use this even when the user is vague (e.g. "I just want to get this running on AWS, you figure it out"). Works for text-generation LLMs, embedding models, rerankers, classifiers, text-to-image / diffusion models — picks the right serving stack and chooses between real-time and async inference. This is the entry-point skill for SageMaker deployment work — it asks clarifying questions, picks a deployment pathway, and coordinates the other deployment skills.

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Ensure a usable SageMaker execution role exists before deploying or training. Use this skill whenever about to create a SageMaker endpoint, model, training job, or any resource that requires an execution role. Use it especially when the user has not provided a role ARN explicitly, when scripts are about to call `iam:CreateRole`, or when an AccessDenied error mentions an IAM action. Never blindly call `iam:CreateRole` — always check for existing roles first. This skill prevents the most common SageMaker deployment failure: trying to create IAM resources from an SSO principal that has no IAM write permissions.

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Create a SageMaker endpoint (real-time or async) with autoscaling, CloudWatch alarms, and tagging enabled by default. Use this skill whenever about to create a SageMaker endpoint, write deployment code that calls `create_endpoint`, or finalize a deployment after the image URI and IAM role are known. Provides deploy.py for real-time endpoints and deploy_async.py for async endpoints (with genuine scale-to-zero support). This is the last step in the SageMaker deployment workflow. Never generate a bare `create_endpoint` call without these defaults — endpoints without autoscaling or alarms are demos, not deployments.

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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.

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 tasks