datasets_goodreads_authors_facets

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

With no annotations, the description carries the full burden of behavioral disclosure. It only mentions that it returns terms aggregation counts and lists the facet enum, but does not explain response structure, how optional filters interact with the aggregation, or any edge cases like invalid facet values or bucket limits.

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 extremely concise, consisting of two short sentences. Every word contributes meaning, and it gets straight to the point without unnecessary filler. The front-loaded phrasing makes the tool's purpose immediately clear.

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?

Given the tool has 7 parameters, no output schema, and no annotations, the description is insufficiently complete. It does not explain the return value structure, nor does it clarify how filtering parameters (q, name, genre, etc.) affect the facet counts, leaving the agent without enough context to predict the tool's behavior.

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 merely repeats the facet enum already present in the schema and adds no new meaning about parameter interactions or usage. As a result, it does not elevate beyond the schema.

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 tool facets the Goodreads authors dataset and returns terms aggregation counts, which conveys a specific verb and resource. It distinguishes itself from sibling datasets_*_search and datasets_*_item tools by focusing on aggregation counts, although it does not explicitly name alternatives.

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?

No guidance is provided on when to use this tool versus the sibling search or item tools. The description implies a use case (getting facet counts) but does not state any exclusions, prerequisites, or alternative tool recommendations.

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