datasets_goodreads_books_facets

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns terms aggregation counts, which is a read-only aggregation behavior. However, it does not provide additional behavioral context such as how many terms are returned, whether counts are approximate, or any filtering effects. The description is not misleading but stays at a basic level.

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 description is concise and front-loaded: it states the purpose in the first clause, then the return type, then the facet enum. Every sentence earns its place, with no wasted words. It is well-structured and easy to parse.

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?

The tool has 18 parameters, 1 required, no output schema, and no annotations. The schema thoroughly documents all input parameters, but the description does not explain the output format beyond a high-level 'terms aggregation counts', nor does it clarify how filter parameters interact with faceting. Given the complexity, the description is minimally complete but leaves room for missing contextual guidance.

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 description adds no parameter semantics beyond what the schema already provides; it merely repeats the facet enum which is already listed in the schema. All 18 parameters have descriptive entries in the schema, so the description does not compensate for any gaps.

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?

The description clearly states the verb 'Facet' and the resource 'Goodreads books dataset', and explicitly says it 'Returns terms aggregation counts'. It also lists the exact facet enum values, distinguishing it from sibling tools like datasets_goodreads_books_search (full-text search) and datasets_goodreads_books_item (single item retrieval), as well as datasets_goodreads_authors_facets (different dataset).

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?

The description implies usage for getting facet counts on the Goodreads books dataset but does not explicitly state when to use this tool instead of alternatives, nor any exclusions. It mentions the facet enum but lacks direct guidance on when to choose this over search or item tools. Usage is implied from the name and description rather than explicitly clarified.

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