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Hugging Face

by co.huggingface

Hub Repository Details

hub_repo_details
Read-only

Get details for one or more Hugging Face repos (model, dataset, or space). Auto-detects type unless specified. For datasets, use operations: overview, dataset_structure, dataset_preview. Use dataset_structure first to discover configs, splits, sizes, and schema. Use dataset_preview only when config and split are known, unless the dataset has a single config/split.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoRow count for dataset_preview. Defaults to 5 and is clamped to 1-100.
splitNoDataset Viewer split. Required for dataset_preview when the dataset has multiple config/split options. Discover via dataset_structure.
configNoDataset Viewer config. Required for dataset_preview when the dataset has multiple config/split options. Discover via dataset_structure.
offsetNoRow offset for dataset_preview. Defaults to 0.
repo_idsYesRepo IDs for (models|dataset/space) - usually in author/name format (e.g. openai/gpt-oss-120b)
repo_typeNoSpecify lookup type; otherwise auto-detects
operationsNoDetails to return. Defaults to ["overview"]. For datasets, prefer ["overview", "dataset_structure"] first; then call ["dataset_preview"] with config and split.

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide readOnlyHint=true and destructiveHint=false, and the description adds behavioral context by explaining auto-detection of repo type and the recommended ordering of dataset operations. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences long, beginning with the core purpose and followed by targeted guidance for datasets. No redundant or filler content; each sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 7 parameters and no output schema, the description covers the dataset workflow well, including discovery and preview conditions. It does not detail return shapes, but given the read-only nature and strong annotations, this is sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers 100% of parameters with descriptions, so baseline is 3. The description adds semantic value by linking operations to parameters (e.g., dataset_structure before dataset_preview, using config/split only when known), exceeding the schema's per-parameter descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Get details for one or more Hugging Face repos (model, dataset, or space)' with a specific verb, resource, and scope. It differentiates from siblings like hub_repo_search by focusing on details retrieval and mentions auto-detection of repo type.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear workflow for datasets: 'Use dataset_structure first to discover configs, splits, sizes, and schema. Use dataset_preview only when config and split are known.' This gives explicit context, though it does not mention when NOT to use the tool or direct alternatives among sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.8/5.0
Disambiguation2/5

hf_fs is a multi-purpose tool with search, ls, stat, and cat subcommands that overlap significantly with hub_repo_search and hub_repo_details. An agent could easily select the wrong tool for searching repos or retrieving details, as both hf_fs and hub_repo_search expose search capabilities and both hf_fs's stat and hub_repo_details provide metadata.

Naming Consistency2/5

The tool names use inconsistent prefixes: 'hf_fs' and 'hf_whoami' versus 'hub_repo_details' and 'hub_repo_search'. This creates two naming conventions (hf_ vs hub_repo_), and hf_fs does not follow the descriptive pattern of the other tools.

Tool Count3/5

With only 4 tools, the count is not excessive, but hf_fs is overloaded with many subcommands while the other tools are narrowly scoped. This makes the tool distribution uneven, though the total number is within a reasonable range.

Completeness3/5

The set covers read-only operations well: searching, listing, reading files, retrieving repo details, and checking auth. However, there are no write operations (create, update, delete) for repositories or files, and dataset preview is only available through hub_repo_details, which might be a gap for dataset-focused workflows.

Resources