Hugging Face

Agents can search the Hugging Face Hub — finding models, datasets, and papers, and reading their documentation and metadata.

Note: This is discovery, not execution. Finding a model through this connector does not run it, download it into a workspace, deploy it, or give an agent inference against it. Running a model is a separate matter from reading about one.

If you want an agent to use a model, that is a model provider connection — see Anthropic, OpenAI, OpenRouter, or Grok.

Before you connect

  • A Hugging Face account.

That is genuinely all for public content. Connect with an account that belongs to your organization if agents should also see private repositories; reach follows the account.

Connect Hugging Face

  1. Open Connectors and select Hugging Face.
  2. On the Access step, choose the identity and which agents may use the connection.
  3. Select Sign in with Hugging Face and complete browser sign-in.

Paperclip registers its client automatically, so there is nothing to configure in a developer console.

Choose access

Two tiers of content, and the difference is worth stating:

Content Who can see it
Public models, datasets, and papers Anyone. Connecting adds no privileged access to these
Private or gated repositories Only if the authorizing account already has access

Because most of the value here is public content, this is one of the lower-risk connectors to give an agent. The exception is an account with access to private organization repositories — that account's reach becomes the agent's.

Paperclip asks Hugging Face for the read-mcp scope, which is what makes this a low-risk connector to start with. Treat that as the intent rather than a guarantee you need not check: the action list is the provider's, so read it on the Permissions tab and set anything that is not a read appropriately. See Set action permissions.

Note: The connection targets the Hub's own tools, and Paperclip requests the endpoint with Gradio tooling switched off — so the interactive applications hosted on Spaces are not part of this connection's surface.

Try it

Search Hugging Face for recent text embedding models under 500M parameters and summarize the top three, including their licences.

Expect results you can check on the Hub. Licence is a useful thing to ask for, because it is exactly the detail people skip when picking a model.

Note: Illustrative task, not a recorded test result.

Troubleshooting and limitations

Problem Likely cause Fix
A private repository is not found The authorizing account does not have access Grant access on the Hub, or connect with an account that has it
A gated model's details are unavailable Gated repositories require accepting terms on the Hub Accept the terms with the authorizing account
An agent claims it can run a model it found It cannot — this connector is discovery only Use a model provider connection
Results seem stale The Hub changes constantly; the agent read what was returned at the time Re-run the search
Needs attention The grant was revoked Select Reconnect

Limitations: discovery and metadata only. No inference, no hosting, no Spaces applications. Private content requires the account to already have access.