hf.hub.model_details
Get full metadata for a HuggingFace model — downloads, likes, tags, library, author, pipeline task, model card data. Use model_id from hf.models search (e.g. "meta-llama/Llama-3.3-70B-Instruct").
This record as markdown: /tools/io-github-whiteknightonhorse-apibase/hf.hub.model-details.md
What hf.hub.model_details does on Apibase
AI agents call hf.hub.model_details to retrieve information from Apibase without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
model_id | string | Yes | Full model ID (e.g. "meta-llama/Llama-3.3-70B-Instruct", "stabilityai/stable-diffusion-xl-base-1.0") |
Parameters from the server's own tool schema.
Why hf.hub.model_details is rated Low
Retrieves publicly available model metadata from HuggingFace Hub without side effects.
From the tool's definition Get full metadata for a HuggingFace model — downloads, likes, tags, library, author.
Attacks that exploit this kind of access
The rule that runs hf.hub.model_details safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Apibase, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For hf.hub.model_details, this is the rule to start with:
hf.hub.model_details is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Apibase, apply this rule, and every hf.hub.model_details call is checked against it from then on.
Questions about hf.hub.model_details
Get full metadata for a HuggingFace model — downloads, likes, tags, library, author, pipeline task, model card data. Use model_id from hf.models search (e.g. "meta-llama/Llama-3.3-70B-Instruct"). It is categorised as a Read tool in the Apibase MCP Server, which means it retrieves data without modifying state.
hf.hub.model_details accepts 1 parameter: model_id. Required: model_id. The full parameter table on this page comes from the server's own tool schema.
Register the Apibase MCP server in PolicyLayer and add a rule for hf.hub.model_details: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Apibase. Nothing to install.
hf.hub.model_details is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the hf.hub.model_details rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for hf.hub.model_details. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
hf.hub.model_details is provided by the Apibase MCP server (apibase-mcp-client). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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