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

Annotations only cover readOnlyHint/openWorldHint, so the description carries the rest and delivers real edge-case behavior: a model with no reviews or an unrecognized model_number returns an empty list rather than an error, and label is a display form of topic rather than translated text. These traits are not derivable from the structured fields.

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?

Purpose is front-loaded and every sentence carries information (vocabulary, downstream wiring, id-vs-SKU, empty-result behavior, label semantics). It is on the dense/long side for a two-parameter tool, but no sentence is filler.

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?

An output schema exists, so return values need not be explained; the description instead covers the things an agent cannot infer: where model_number comes from, that the vocabulary is per model, and how empty results are signaled. Nothing needed to call it correctly is missing.

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 0%, so the description must compensate, and it does for the critical parameter: model_number is the products[].model_number value from adidas_search, explicitly NOT products[].id, with a worked example. The locale parameter is only indirectly addressed via the note that labels are the same English values for every locale.

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+resource: the filterable review topics for one product model, with a concrete sample vocabulary. It is immediately distinguishable from siblings adidas_product_reviews, adidas_product and adidas_search because it names the downstream tool it feeds.

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 instructs feeding a topics[].topic value back to adidas_product_reviews as its topic argument, warns against hard-coding the vocabulary because it is per model, and names adidas_search as the source of model_number. This is exactly the when-to-use/when-not guidance the rubric asks for.

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