Hugging Face MCP Server
Explore AI models, datasets and Spaces via Hugging Face — search models, inspect files, review discussions and track collections from any AI agent.
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What is the Hugging Face MCP Server?
The Hugging Face MCP Server gives AI agents like Claude, ChatGPT, and Cursor direct access to Hugging Face via 13 tools. Explore AI models, datasets and Spaces via Hugging Face — search models, inspect files, review discussions and track collections from any AI agent. Powered by the Vinkius - no API keys, no infrastructure, connect in under 2 minutes.
Built-in capabilities (13)
Tools for your AI Agents to operate Hugging Face
Ask your AI agent "Find popular text generation models with over 1000 likes." and get the answer without opening a single dashboard. With 13 tools connected to real Hugging Face data, your agents reason over live information, cross-reference it with other MCP servers, and deliver insights you would spend hours assembling manually.
Works with Claude, ChatGPT, Cursor, and any MCP-compatible client. Powered by the Vinkius - your credentials never touch the AI model, every request is auditable. Connect in under two minutes.
Why teams choose Vinkius
One subscription gives you access to thousands of MCP servers - and you can deploy your own to the Vinkius Edge. Your AI agents only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure and security, zero maintenance.
Build your own MCP Server with our secure development framework →Vinkius works with every AI agent you already use
…and any MCP-compatible client
Hugging Face MCP Server capabilities
13 toolsRequires the repo type (model, dataset or space), the repo ID in "author/name" format and the discussion title. Returns the created discussion with its ID, title and URL. Create a new discussion on a Hugging Face repo
Provide the collection slug. Get details for a specific Hugging Face collection
Provide the model ID in "author/name" format (e.g. "google-bert/bert-base-uncased"). Get details for a specific Hugging Face model
Tags include framework (pytorch, tensorflow), license, dataset, language and task-specific labels. The pipeline_tag indicates the model's primary task (e.g. "text-generation", "image-classification", "translation"). Get tags and pipeline info for a Hugging Face model
Provide the space ID in "author/name" format. Get details for a specific Hugging Face Space
Returns user name, avatar, organizations, auth type, plan and access tokens metadata. Use this to verify your token is working correctly. Get the authenticated Hugging Face user
Optionally filter by author and limit. Returns collection slug, title, description, author, item count and likes count. List collections on Hugging Face Hub
Returns filenames (e.g. "train.parquet", "test.parquet", "data/", "README.md"). Optionally set a subdirectory path. Useful for understanding dataset structure before downloading. List files in a Hugging Face dataset repository
Optionally filter by search term, author and limit. Returns dataset ID, author, description, download count, likes count and creation date. List datasets on Hugging Face Hub
Returns discussion title, author, creation date, number of comments and whether it is resolved. Use this to review community feedback, bug reports and feature requests for a model. List discussions for a Hugging Face model
Returns filenames, file sizes and paths (e.g. "model.safetensors", "tokenizer.json", "config.json", "README.md"). Optionally set a subdirectory path to list files within a specific folder. Useful for inspecting model artifacts and understanding the repository structure. List files in a Hugging Face model repository
Optionally filter by search term (free-text across model cards), author (organization or username) and limit the number of results. Returns model ID, author, pipeline task tag, download count, likes count and creation date. List models on Hugging Face Hub
Optionally filter by search term, author and limit. Returns space ID, title, author, SDK (Gradio, Streamlit, Docker), likes count and creation date. List Spaces on Hugging Face Hub
What the Hugging Face MCP Server unlocks
Connect your Hugging Face account to any AI agent and explore the world's largest AI model hub through natural conversation.
What you can do
- Model Discovery — Search and browse thousands of models by name, task type, framework and author
- Model Inspection — View model metadata including pipeline task, tags, download counts, likes and file structure
- Dataset Exploration — Find and inspect datasets with their descriptions, sizes and file trees
- Spaces Gallery — Browse ML demo apps (Gradio, Streamlit, Docker) and check their runtime status
- Collections — View curated collections of models, datasets and spaces organized by topic
- Community Discussions — Read model discussion threads for bug reports, feature requests and usage tips
- File Tree Browsing — List repository files (model weights, configs, tokenizers) without downloading
How it works
1. Subscribe to this server
2. Enter your Hugging Face Access Token
3. Start exploring the ML hub from Claude, Cursor, or any MCP-compatible client
No more switching to the browser to check model tags or browse discussion threads. Your AI acts as a dedicated ML researcher.
Who is this for?
- ML Engineers — quickly find models by task type, inspect their tags and file structure, and review community discussions before integration
- Researchers — browse datasets, explore collections and discover related models without leaving your notebook
- Developers — check Space runtime status, review model files and find suitable models for your application via conversation
Frequently asked questions about the Hugging Face MCP Server
How do I get a Hugging Face Access Token?
Log in to Hugging Face, go to Settings > Access Tokens, click New token, give it a name and select scopes (read is sufficient for browsing, write if you need to create repos). Copy the token immediately — it starts with hf_.
Can I search models by task type (e.g. text-generation)?
Yes! Use list_models with a search query. While the search endpoint doesn't directly filter by pipeline_tag, you can search by task name (e.g. search='text-generation') and then use get_model or get_model_tags to verify the pipeline_tag of specific models.
Can I see what files are in a model repository?
Yes! Use list_model_files with the model ID (e.g. 'google-bert/bert-base-uncased') to see the complete file tree including model weights (.safetensors, .bin), config files, tokenizer files and README. Optionally set a path to browse a specific subdirectory like 'onnx' or 'pytorch'.
Can I create discussions on model pages?
Yes! Use create_discussion with the repo type ('model', 'dataset' or 'space'), the repo ID and a title. This creates a new discussion thread on the repository. You can use list_model_discussions first to check existing threads before creating a new one.
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Connect Hugging Face with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
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Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
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Data-aware AI agent framework for structured and unstructured sources.
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Give your AI agents the power of Hugging Face MCP Server
Production-grade Hugging Face MCP Server. Verified, monitored, and maintained by Vinkius. Ready for your AI agents — connect and start using immediately.