adidas_product_reviews

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

With no annotations provided, the description carries the full burden, and it delivers: it discloses pagination (10 per page), locale-based filtering of review text, the fact that non-English locales commonly return empty reviews lists, and the 200-with-zero-count behavior for unrecognized model numbers. It also reveals the AI-generated digest is included only 'when one exists'. These are behavioral traits an agent cannot infer from the schema.

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 dense but not bloated; every sentence adds information. It front-loads the core purpose and return contents, then covers the key identifier caveat and edge-case behaviors. It is longer than the minimum, but the length is justified by the number of non-obvious behaviors (locale scoping, empty-list semantics, model_number vs SKU) that an agent needs to know.

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?

For a read-only reviews tool with no output schema and no annotations, the description is remarkably complete. It covers what is returned, how to get the required parameter, pagination, locale effects, and error semantics. The only minor omission is a note about rate limits or authentication, but those are not mentioned for sibling tools either and the description covers all operationally critical aspects.

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, but the description adds value beyond the schema: it clarifies that model_number is the Adidas model number, not the SKU, and explains the practical consequence of locale on the reviews list. The page, topic, rating, and locale parameters are already well-described in the schema, so the description's marginal addition is focused and useful.

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 opens with a specific verb and resource ('Get Adidas product reviews'), then enumerates exactly what is returned: one page of reviews plus the rating summary components (overall rating, star histogram, recommend percentage, per-attribute averages, AI digest). It also distinguishes the model_number from SKU and points to adidas_search as the source, which separates it from sibling tools like adidas_product_review_topics and adidas_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 explicitly states when to use this tool and how to obtain the required identifier: 'take it from an adidas_search result's products[].model_number field'. It also explains the locale scoping behavior and the empty-reviews behavior for unrecognized model numbers, which tells an agent what to expect and when to look elsewhere. It does not name an alternative tool for reviews, but the guidance is concrete and actionable.

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