Search and Deploy AI Models from Hugging Face Hub
MCP server that connects AI assistants to Hugging Face Hub to search models, datasets, Spaces, papers, fetch documentation, run GPU compute jobs, and invoke
Why it matters
Connect AI assistants to the Hugging Face Hub to discover, compare, and deploy machine learning models, datasets, and Spaces, while running compute jobs on cloud GPUs and accessing documentation for ML libraries.
Outcomes
What it gets done
Search and compare models, datasets, Spaces, and research papers across the Hugging Face Hub
Fetch repository details, README files, and library documentation for ML frameworks
Run Python scripts and training jobs on cloud GPUs with configurable compute flavors
Invoke Gradio Spaces as AI tools for tasks like image generation and audio transcription
Install
Add it to your toolbox
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/ag-hf-mcp | bash Overview
Hugging Face MCP Server
The Hugging Face MCP Server connects AI assistants to the Hugging Face Hub. It enables searching across models, datasets, Spaces, and papers; fetching repository details and documentation; running compute jobs on cloud GPUs; and invoking Gradio Spaces as callable AI tools. Use this skill when you need to discover ML resources (finding the best model for code generation, comparing Llama vs Qwen models, locating sentiment analysis datasets), access Hugging Face documentation (learning how to fine-tune with LoRA using PEFT), run compute workloads on cloud infrastructure (training scripts on A10G GPUs, quick GPU jobs), or use Gradio applications as tools (background removal, speech transcription, image generation). Use it when connected to the HF MCP server. Do not use this skill when the task falls outside Hugging Face Hub's scope. Verify commands, API behavior, pr
What it does
The Hugging Face MCP Server connects AI assistants to the Hugging Face Hub. It enables searching across models, datasets, Spaces, and papers; fetching repository details and documentation; running compute jobs on cloud GPUs; and invoking Gradio Spaces as callable AI tools.
When to use - and when NOT to
Use this skill when you need to discover ML resources (finding the best model for code generation, comparing Llama vs Qwen models, locating sentiment analysis datasets), access Hugging Face documentation (learning how to fine-tune with LoRA using PEFT), run compute workloads on cloud infrastructure (training scripts on A10G GPUs, quick GPU jobs), or use Gradio applications as tools (background removal, speech transcription, image generation). Use it when connected to the HF MCP server.
Do not use this skill when the task falls outside Hugging Face Hub's scope. Verify commands, API behavior, pricing, quotas, credentials, and deployment effects against current official documentation before making changes. Do not treat generated examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.
Inputs and outputs
Users provide natural language requests like "Find the best model for code generation" or "Run this training script on an A10G." The skill translates these into MCP tool calls with specific parameters (search queries, repository IDs, job configurations, Space invocations). It returns search results, repository details with READMEs, documentation pages, job status and logs, or outputs from Gradio Space invocations.
Integrations
model_search: Discovers models by task, author, query, sorted by trending score or downloads. Supports filtering by tags and limits.
dataset_search: Finds datasets with query terms, language tags, task categories, sorted by downloads or other metrics.
space_search: Locates Gradio Spaces and applications, with mcp=true flag to filter for Spaces usable as tools.
paper_search: Searches academic papers on the Hub with configurable result limits.
hub_repo_details: Fetches repository metadata and READMEs for models, datasets, or Spaces. Supports batch queries and include_readme=true for full documentation.
hf_doc_search and hf_doc_fetch: Searches and retrieves documentation for Hugging Face libraries (transformers, peft, diffusers, etc.).
hf_jobs: Runs compute jobs on cloud GPUs/CPUs with operations including run (custom Docker images), uv (inline Python scripts), ps (list jobs), logs (fetch output), and scheduled uv (cron jobs). Supports flavors like t4-small, a10g-small, cpu-basic and secret injection for private repos.
dynamic_space: Invokes Gradio Spaces as tools with operations discover (list available tasks), view_parameters (inspect Space inputs), and invoke (call with parameters).
gr1_flux1_schnell_infer: Generates images from text prompts.
hf_whoami: Checks authentication status.
Setup instructions: https://huggingface.co/settings/mcp
Who it's for
ML engineers and researchers who need to discover, evaluate, and compare models or datasets across providers. Data scientists searching for training data with specific language or task requirements. Developers building AI applications that need to invoke Gradio Spaces programmatically as backend tools. Teams running training or inference workloads on cloud GPUs without managing infrastructure. Technical writers and learners querying Hugging Face library documentation.
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)
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)
Source README
Hugging Face MCP Server
When to Use
Use this skill when you need 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.
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": "$HF_TOKEN"}
})
Check Job Status
User: "What's happening with my training job?"
1. hf_jobs(operation="ps")
2. hf_jobs(operation="logs", args={"job_id": "job-xxxxx"})
Explore What's Trending
User: "What models are trending right now?"
model_search(sort="trendingScore", limit=20)
Get Model Card Details
User: "Tell me about Mistral-7B"
hub_repo_details(repo_ids=["mistralai/Mistral-7B-v0.1"], include_readme=true)
Find Quantized Models
User: "Find GGUF versions of Llama 3"
model_search(query="Llama 3 GGUF", sort="downloads", limit=10)
Use a Gradio Space as a Tool
User: "Transcribe this audio file"
1. space_search(query="speech to text transcription", mcp=true)
2. dynamic_space(operation="view_parameters", space_name="openai/whisper")
3. dynamic_space(operation="invoke", space_name="openai/whisper", parameters="{\"audio\": \"...\"}")
Schedule Recurring Jobs
User: "Run this data sync every day at midnight"
hf_jobs(operation="scheduled uv", args={
"script": "...",
"cron": "0 0 * * *",
"flavor": "cpu-basic"
})
Tool Selection Guide
| Goal | Tool |
|---|---|
| Find models | model_search |
| Find datasets | dataset_search |
| Find Spaces/apps | space_search |
| Find papers | paper_search |
| Get repo README/details | hub_repo_details |
| Learn library usage | hf_doc_search → hf_doc_fetch |
| Run code on GPU/CPU | hf_jobs |
| Use Gradio apps as tools | dynamic_space |
| Generate images | gr1_flux1_schnell_infer or dynamic_space |
| Check auth | hf_whoami |
Tips
- Use
sort="trendingScore"to find what's popular now - Use
sort="downloads"to find battle-tested options - Set
mcp=trueinspace_searchto find Spaces usable as tools - Use
include_readme=trueinhub_repo_detailsfor full model/dataset documentation - For jobs accessing private repos, always include
secrets: {"HF_TOKEN": "$HF_TOKEN"} - Use
dynamic_space(operation="discover")to see all available Space-based tasks
Limitations
- Use this skill only when the task clearly matches its upstream product or API scope.
- Verify commands, API behavior, pricing, quotas, credentials, and deployment effects against current official documentation before making changes.
- Do not treat generated examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.
FAQ
Common questions
Discussion
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