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

Annotations already cover the safe-read profile (readOnlyHint/openWorldHint), but the description adds real behavior: 10 reviews per page, locale affects both summary labels and review language, and an unrecognized model_number yields an empty list rather than an error. The non-error edge case is genuinely useful disclosure; auth and rate-limit details are absent but likely not needed for a read tool.

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?

Front-loaded with the purpose and return contents, then the critical identifier caveat, then parameters and the edge case. It is dense but each sentence carries information; no obvious 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 needn't be spelled out, yet the description still summarizes them usefully. The only meaningful gap is the undocumented 'topic' parameter; otherwise an agent has everything needed to call it correctly.

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 0%, so the description carries the full burden, and it does well: it documents model_number format/derivation, page size, rating (1-5 star filter), and locale default/semantics. The 'topic' parameter, however, is never mentioned in either schema or description, leaving one of five parameters opaque.

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 ('Adidas customer reviews for one product model') and enumerates the exact payload: rating summary, star histogram, recommend percentage, per-attribute averages, and AI digest. This clearly distinguishes it from siblings like adidas_product and adidas_product_review_topics.

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 a concrete usage path: model_number comes from an adidas_search result's products[].model_number, explicitly warning it is NOT products[].id. It also states the empty-result case, which guides expectation. It does not, however, name the sibling tool (adidas_product_review_topics) as an alternative, so routing among siblings is left to inference.

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