datasets_apple_podcasts_shows_facets

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

With no annotations, the description carries the burden of behavioral disclosure. It states the primary behavior (returns terms aggregation counts) and enumerates valid facets, which is helpful. However, it doesn't disclose how filters interact with the facet computation, response format, or any limitations like default sizes or unsupported combinations.

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 compact and front-loaded with the main action. It uses a clean code block for the facet enum, making allowed values easy to scan. Every sentence earns its place without redundancy or fluff.

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 core behavior and allowed facets are explained, and the schema covers parameters. However, there is no output schema, and the description doesn't outline the response shape beyond vague 'counts', nor does it clarify how optional filters (q, genre, country) affect the aggregation. This leaves gaps for an agent needing to predict the tool's output.

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 baseline is 3. The description lists the facet enum values, but the schema's facet parameter already includes the same enum list, adding no new meaning. The other parameters' meanings are fully captured in the schema, so the description doesn't elevate understanding.

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 Apple Podcasts shows dataset and returns terms aggregation counts. It lists the specific facet enum values, distinguishing it from sibling search and item tools like apple_podcasts_show or datasets_apple_podcasts_shows_search.

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 explains what the tool does but provides no guidance on when to use it versus alternatives (e.g., datasets_apple_podcasts_shows_search). It doesn't mention use cases, prerequisites, or exclusions, leaving the agent to infer from the name.

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