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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety/open-world profile is covered. The description adds the useful constraint that counts are computed over the stored dataset, but says nothing about pagination or result shape for the facet counts themselves.

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 packed sentences: the first states the action and examples, the second handles disambiguation. Nothing is wasted and the core purpose is front-loaded.

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 an output schema present and annotations covering safety, the description needn't explain return values, and it correctly focuses on purpose plus sibling routing. Given 11 parameters it could hint at how filters narrow the aggregation, but the essentials for correct invocation are present.

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 the schema already documents all 11 parameters including the required facet and its allowed values. The description only restates example facet dimensions already present in the schema, adding no syntax or meaning beyond 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?

Names a specific verb (count facet values), resource (Apple Podcasts shows dataset) and concrete facet examples (genre, country). It also names the sibling it is not, so an agent can distinguish it from datasets_apple_podcasts_shows_search without opening any 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?

Explicitly routes the agent to the alternative: 'Use datasets_apple_podcasts_shows_search for matching show records,' which contrasts aggregation against record retrieval. It lacks a when-not exclusion beyond that single alternative, so it falls short of a full 5.

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