nike_product_availability

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

No annotations are provided, so the description carries the responsibility of behavioral disclosure, and it does a solid job. It explains that the data is sourced from the same anonymous mobile backend Nike's app uses (raising authenticity expectations), and it enumerates exactly what each size record includes: label, localized label, color variant, GTIN, available flag, shipping-availability level (HIGH/LOW/MEDIUM/OOS), and width grouping. It also discloses that store pickup is out of scope. It doesn't mention response shape for the overall payload, but there is no output schema and the field list is substantial.

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 a single dense paragraph that leads with the core purpose and then packs useful details into subsequent sentences. Every sentence contributes: what it returns, where the data comes from, how to get the parameter, what fields are present, and what is excluded. It is longer than strictly necessary, but the detail is high-value, and there is no filler or repetition.

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 one-parameter tool with no annotations and no output schema, the description is unusually complete. It tells the agent what the output contains, where the input comes from, and what availability scope to expect. The only notable gap is that it doesn't describe the top-level response envelope or pagination, which is minor for a per-product availability lookup.

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% for the single parameter group_key, and the description reinforces that group_key is the product's rollup key and tells the agent exactly where to obtain it (a search result's products[].group_key field). That is meaningful added context beyond the schema's bare description, so the description earns credit above the baseline 3.

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 object — 'Get Nike product size availability' — and differentiates itself clearly by noting it returns per-size shipping availability for a single product from Nike's mobile backend. It names the exact input (group_key) and the return fields, so an agent can distinguish it from siblings like nike_product_details and nike_product without opening schemas.

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 states a clear context for use — when you need per-size shipping availability for one Nike product — and explains how to obtain the required group_key (from a search result's products[].group_key field). It also explicitly notes what is NOT included (per-store pickup availability), which helps an agent avoid using it for store-level queries. However, it doesn't explicitly name sibling alternatives or state when NOT to use it beyond the store-pickup exclusion.

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