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Vinkius
CursorIDE
Why use Hugging Face MCP Server with Cursor?

Bring Model Discovery
to Cursor

Create your Vinkius account to connect Hugging Face to Cursor and start using all 13 AI tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code. No hosting, no server setup — just connect and start using.

MCP Inspector GDPR Free for Subscribers
Create DiscussionGet CollectionGet ModelGet Model TagsGet SpaceGet UserList CollectionsList Dataset FilesList DatasetsList Model DiscussionsList Model FilesList ModelsList Spaces
ChatGPT Claude Perplexity

Compatible with every major AI agent and IDE

ClaudeClaude
ChatGPTChatGPT
CursorCursor
GeminiGemini
WindsurfWindsurf
VS CodeVS Code
JetBrainsJetBrains
VercelVercel
+ other MCP clients
Hugging Face

What is the Hugging Face MCP Server?

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

Built-in capabilities (13)

create_discussion

Requires 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

get_collection

Provide the collection slug. Get details for a specific Hugging Face collection

get_model

Provide the model ID in "author/name" format (e.g. "google-bert/bert-base-uncased"). Get details for a specific Hugging Face model

get_model_tags

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

get_space

Provide the space ID in "author/name" format. Get details for a specific Hugging Face Space

get_user

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

list_collections

Optionally filter by author and limit. Returns collection slug, title, description, author, item count and likes count. List collections on Hugging Face Hub

list_dataset_files

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

list_datasets

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

list_model_discussions

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

list_model_files

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

list_models

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

list_spaces

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

Why Cursor?

Cursor's Agent mode turns Hugging Face into an in-editor superpower. Ask Cursor to generate code using live data from Hugging Face and it fetches, processes, and writes. all in a single agentic loop. 13 tools appear alongside file editing and terminal access, creating a unified development environment grounded in real-time information.

  • Agent mode turns Cursor into an autonomous coding assistant that can read files, run commands, and call MCP tools without switching context

  • Cursor's Composer feature can generate entire files using real-time data fetched through MCP. no copy-pasting from external dashboards

  • MCP tools appear alongside built-in tools like file reading and terminal access, creating a unified agentic environment

  • VS Code extension compatibility means your existing workflow, keybindings, and extensions all work alongside MCP tools

See it in action

Hugging Face in Cursor

AI AgentVinkius
High Security·Kill Switch·Plug and Play
Enterprise Security

Why run Hugging Face with Vinkius?

The Hugging Face connection runs on our fully managed, secure cloud infrastructure. We handle the hosting, maintenance, and security so you don't have to deal with servers or code. All 13 tools are ready to work instantly without any complex setup.

You stay in complete control of your data. Your AI only accesses the information you approve, keeping your sensitive passwords and private details completely safe. Plus, with automatic optimizations, your AI works faster and more efficiently.

Hugging Face
Fully ManagedNo server setup
Plug & PlayNo coding needed
SecurePrivacy protected
PrivateYour data is safe
Cost ControlBudget limits
Control1-click disconnect
Auto-UpdatesMaintenance free
High SpeedOptimized for AI
Reliable99.9% uptime
Your credentials and connection tokens are fully encrypted

* Every connection is hosted and maintained by Vinkius. We handle the security, updates, and infrastructure so you don't have to write code or manage servers. See our infrastructure

01 / Catalog

Over 4,000 integrations ready for AI agents

Explore a vast library of pre-built integrations, optimized and ready to deploy.

02 / Credentials

Connect securely in under 30 seconds

Generate tokens to authenticate and link external services in a single step.

03 / Guardian

Complete visibility into every agent action

Audit live requests, latency, success rates, and active security compliance policies.

04 / FinOps

Optimize spending and track token ROI

Analyze real-time token consumption and cost metrics detailed by connection.

Over 4,000 integrations ready for AI agents
Connect securely in under 30 seconds
Complete visibility into every agent action
Optimize spending and track token ROI

Explore our live AI Agents Analytics dashboard to see it all working

This dashboard is included when you connect Hugging Face using Vinkius. You will never be left in the dark about what your AI agents are doing with your tools.

Why Vinkius

Hugging Face and 4,000+ other AI tools. No hosting, no code, ready to use.

Professionals who connect Hugging Face to Cursor through Vinkius don't need to write code, manage servers, or worry about security. Everything is pre-configured, secure, and runs automatically in the background.

4,000+MCP Integrations
<40msResponse time
100%Fully managed
Raw MCP
Vinkius
Ready-to-use MCPsFind and configure each manually4,000+ MCPs ready to use
Connection SetupManual coding & server setup1-click instant connection
Server HostingYou host it yourself (needs 24/7 uptime)100% hosted & managed by Vinkius
Security & PrivacyStored in plaintext config filesBank-grade encrypted vault
Activity VisibilityBlind execution (no logs or tracking)Live dashboard with real-time logs
Cost ControlRunaway AI token spend riskAutomatic budget limits
Revoking AccessMust delete files or code to stop1-click disconnect button
The Vinkius Advantage

How Vinkius secures Hugging Face for Cursor

Every request between Cursor and Hugging Face is protected by our secure gateway. We automatically keep your sensitive data private, prevent unauthorized access, and let you disconnect instantly at any time.

< 40msCold start
Ed25519Signed audit chain
60%Token savings
FAQ

Frequently asked questions

01

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_.

02

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.

03

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'.

04

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.

05

What is Agent mode and why does it matter for MCP?

Agent mode is Cursor's autonomous execution mode where the AI can perform multi-step tasks: reading files, editing code, running terminal commands, and calling MCP tools. Without Agent mode, Cursor operates in a simpler ask-and-answer mode that doesn't support tool calling. Always ensure you're in Agent mode when working with MCP servers.

06

Where does Cursor store MCP configuration?

Cursor looks for MCP server configurations in a mcp.json file. You can configure servers at the project level (.cursor/mcp.json in your project root) or globally (~/.cursor/mcp.json). Project-level configs take precedence.

07

Can Cursor use MCP tools in inline edits?

No. MCP tools are only available in Agent mode through the chat panel. Inline completions and Tab suggestions do not trigger MCP tool calls. This is by design. tool calls require user visibility and approval.

08

How do I verify MCP tools are loaded?

Open Settings → Features → MCP and look for your server name. A green indicator means the server is connected. You can also check Agent mode's available tools by clicking the tools dropdown in the chat panel.

09

Tools not appearing in Cursor

Ensure you are in Agent mode (not Ask mode). MCP tools only work in Agent mode.

10

Server shows as disconnected

Check Settings → Features → MCP and verify the server status. Try clicking the refresh button.

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