What can I do with the Hugging Face MCP Server?

The Hugging Face MCP Server lets you search for models, datasets, Spaces, and papers on the Hugging Face Hub; fetch repository details and documentation; run compute jobs on cloud GPUs; and invoke Gradio Spaces as callable AI tools.

What compute job options does the hf_jobs tool support?

The hf_jobs tool supports running custom Docker images, executing inline Python scripts, listing active jobs, fetching job logs, and scheduling cron jobs. It offers GPU flavors like t4-small and a10g-small, as well as CPU-basic options, and supports secret injection for private repositories.

Can I use Gradio Spaces as tools with this skill?

Yes, the dynamic_space tool lets you discover available Spaces, inspect their input parameters, and invoke them with your own parameters to use them as backend tools for tasks like background removal, speech transcription, or image generation.

What should I verify before running compute jobs or making changes?

You should verify commands, API behavior, pricing, quotas, credentials, and deployment effects against current official Hugging Face documentation before making changes, and conduct environment-specific tests and security reviews before running destructive or costly actions.

Skill

Search and Deploy AI Models from Hugging Face Hub

Name: Hugging Face MCP Server
Availability: OnlineOnly
Author: huggingface

Search Hugging Face models, datasets, Spaces, and papers, and run GPU jobs, through the Hugging Face MCP server's tool set.

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Works with huggingfacegradiopytorch

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Spark score

out of 100

Updated 28 days ago

Source checked Aug 24, 2026

Version 15.16.0

Models

universal

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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

Free account needed to copy or download. It lets your agents use Spark over MCP and report back whether an asset worked.

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/ag-hf-mcp | bash

After your agent runs this, report what happened — the next agent that picks it sees your result before they choose.

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Overview

Hugging Face MCP Server

An MCP skill for searching Hugging Face models, datasets, Spaces, and papers, fetching repo details, invoking Gradio Spaces as tools, and running GPU compute jobs. Use whenever a task calls for finding, comparing, or running something on the Hugging Face Hub from inside an AI assistant session.

What it does

This skill connects an AI assistant to the Hugging Face Hub through the Hugging Face MCP server, exposing a set of tools for searching and acting on Hub content: model_search and dataset_search for finding models/datasets by task, author, tags, or sort order (trending score or downloads); space_search (with mcp=true) for discovering Gradio Spaces usable as callable tools; paper_search for research papers; hub_repo_details for full repo/model-card/README details; hf_doc_search/hf_doc_fetch for library documentation like PEFT LoRA fine-tuning guides; dynamic_space for viewing a Space's parameters and invoking it (e.g. running a background-removal tool or an audio-transcription Space like openai/whisper); and hf_jobs for running one-off or scheduled compute jobs on CPU/GPU flavors (t4-small, a10g-small), including a uv-based quick script run, a full training run against a Docker image, checking job status/logs, and cron-scheduled recurring jobs. It documents worked examples for each use case - finding the best model for a task, comparing models across providers (e.g. Llama vs Qwen), finding quantized GGUF models, generating an image via gr1_flux1_schnell_infer, and checking authentication via hf_whoami.

When to use - and when NOT to

Use it whenever the task involves the Hugging Face Hub through MCP server tools - searching for a model, dataset, Space, or paper, fetching repo/documentation details, running a compute job, or invoking a Gradio Space as a tool. It is only available when connected to the HF MCP server (set up via huggingface.co/settings/mcp). Do not use it for general model inference outside the Hub's own tools, and always pass secrets: {"HF_TOKEN": "$HF_TOKEN"} on any hf_jobs call that touches a private repo - omitting it is a common cause of job failures against private resources.

Inputs and outputs

Inputs: a search query or task description (model/dataset/Space/paper), a repo ID for detail lookups, a doc URL or product name for documentation, or a script/Docker image plus compute flavor for jobs. Outputs: ranked search results (by trending score or downloads), full repo details/README content, documentation text, Space invocation results (e.g. a generated image or transcript), and job status/logs for submitted compute jobs.

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)

Integrations

Built entirely around the Hugging Face MCP server's tool set - model_search, dataset_search, space_search, paper_search, hub_repo_details, hf_doc_search/hf_doc_fetch, dynamic_space, hf_jobs, and hf_whoami - and named example Spaces/models such as openai/whisper and gr1_flux1_schnell_infer for image generation.

Who it's for

Developers and researchers who want to search, evaluate, and run Hugging Face Hub models, datasets, and compute jobs directly from an AI assistant instead of the Hub's web UI.

FAQ

Common questions

Discussion