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

Annotations already declare readOnlyHint and openWorldHint, so the safety profile is covered. The description adds genuine behavioral context that annotations don't: it returns aggregation counts rather than records and honors the same filter semantics as search. Return shape is left to the output schema, which exists.

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?

Two tightly written sentences with no redundancy; the core behavior is front-loaded before the usage hint. Every clause earns its 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?

Given 21 parameters, full schema coverage, an output schema, and annotations, the description supplies what the schema cannot: the aggregation behavior and the relationship to the search tool. It is adequate, though naming the sibling tool explicitly would have made routing unambiguous.

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% and every parameter, including the required 'facet' enum values, is documented in the schema itself. The description only notes that one field is aggregated and that filters mirror search, adding no syntax or value detail beyond the schema, so baseline 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 ('Returns terms-aggregation counts for one field over the Goodreads books dataset'), which clearly distinguishes it from record-returning tools. It does not name the sibling search tool explicitly, only referring to 'the related search tool', so it stops short of full sibling differentiation.

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?

Gives clear context: use it alongside the search tool to inspect filter counts under the same query filters. This tells the agent when the tool is valuable, but it never names the sibling (datasets_goodreads_books_search) nor states any when-not-to-use conditions.

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