Dataset licence facts

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

With no annotations, the description carries the full disclosure burden and does a lot: it explains the two data paths (daily build vs live Hub check), that each value carries an evidence URL with commit-specific links, exactly what each signal value means, and it explicitly warns that signals do not establish permission. It omits failure behavior for private/nonexistent datasets and any rate-limit or latency implications of live checks.

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

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

Front-loaded with the verb and returned fields, and the signal glossary is genuinely useful. Some redundancy: 'These signals do not establish permission to use a dataset' and the closing 'Not legal advice, and not a grant of permission' say the same thing twice, and 'Licence facts reported by the dataset and its declared sources' restates the opening.

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

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

With no output schema, the description does the work of enumerating return values, evidence URL semantics, and signal meanings, which is what an agent needs. The remaining gap is edge-case behavior for datasets that cannot be checked (private, gated, or nonexistent), which matters because the live path can fail.

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

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

Schema description coverage is 100%, so both parameters are already documented, including the live flag's semantics. The description's statement that build data is used by default for build datasets adds slight nuance about the default, but largely restates the schema, so the baseline of 3 applies.

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

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

States a specific verb and resource ('Retrieve licence facts for one Hugging Face dataset') and enumerates the exact payload: declared licence, declaration location, source datasets, licence files, signal, plus evidence URLs. An agent can distinguish this from the only sibling, find_datasets, which is a search tool, without opening either schema.

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

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

Useful build-vs-live guidance ('For datasets in the daily build, facts come from the build by default. Other public datasets are checked live on the Hub') tells the agent when the default path applies. However, there is no explicit statement of when to use this tool versus find_datasets, and no prerequisites for the required id (e.g. private/gated datasets).

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

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

With no annotations, the description carries the burden well: it discloses the corpus (top 5,000, daily build), result ordering, the 50-result cap, AND semantics across filters, and what licence_evidence points to. It also flags that signals are evidence, not permission — an important caveat for a licence tool. Missing only response shape beyond licence_evidence and any freshness/latency notes.

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

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

The core purpose and filters are correctly front-loaded. However, the four signal definitions duplicate the schema enum verbatim, and the non-permission disclaimer is stated twice ('These signals do not establish permission...' and 'not a grant of permission to use the dataset'), so several sentences do not earn their place.

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

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

With four optional params and no output schema, the description covers ordering, the result cap, filter combination logic, signal meanings and the licence_evidence pointer, which is enough to call it correctly. It stops short of describing the other fields in each returned dataset record, the one remaining gap.

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

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

Schema coverage is 100%, so the baseline is 3; the signal enum text is copied verbatim from the schema and adds nothing. The one genuine addition is explaining licence_evidence semantics, which compensates slightly because there is no output schema to document it.

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

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

States a specific verb and resource with scope: 'Search the 5,000 most downloaded Hugging Face datasets in the daily build, returning matches in descending order of download count.' That is far more precise than a generic search tool, but it never names or contrasts with the sibling dataset_licence, so sibling differentiation is left to inference.

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

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

Explains that filters are optional and conjunctive ('all supplied filters must match') and caps results at 50, which is useful invocation context. It gives no explicit when-to-use/when-not guidance and never mentions the sibling dataset_licence as the alternative for licence detail on a single dataset.

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