nike_product_recommendations

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

With no annotations provided, the description carries the full behavioral burden and excels: it discloses the data source (Nike's own anonymous mobile backend), the exact return payload (style-color, rank, title/subtitle, image, PDP URL, current pricing), and the critical fallback behavior where an unrecognized style_color returns Nike's fallback recommendations rather than an empty list or error. This prevents an agent from misinterpreting fallback results as a failure or expecting keyword-matched relevance.

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?

Four sentences, each earning its place: core action, provenance and scope, parameter sourcing, and output contents with behavioral caveats. The content is front-loaded with the primary purpose first, and the length is justified by the need to disclose fallback and ranking semantics that the absent annotations cannot carry.

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 single-parameter tool with no annotations and no output schema, the description is thorough: it explains how to obtain the input, what each recommendation contains, the ranking semantics, and the surprising fallback behavior. Minor omissions like pagination and pricing currency are negligible for a recommendations endpoint.

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% — the style_color parameter is already documented in the schema as the anchor product's style-color id from a search result's colors[].style_color field. The description repeats essentially the same definition verbatim, reinforcing it but adding no new format, pattern, or validation details, so the high-coverage baseline of 3 applies.

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 Nike product recommendations') and immediately narrows the scope: related-product ('Shop Similar') recommendations for one anchor product. This clearly distinguishes it from Nike siblings like nike_search, nike_product, and nike_product_reviews without requiring the agent to open any other 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?

The description tells the agent exactly where the input comes from ('from a search result's colors[].style_color field'), establishing the prerequisite workflow of searching first. It also signals semantic boundaries with 'not a guaranteed keyword match,' and warns what to expect for unrecognized IDs. However, it never names alternative tools outright or states explicit when-not-to-use conditions.

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