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

Annotations already declare readOnlyHint=true and openWorldHint=true, so safety is covered. The description adds genuine behavioral context beyond that: it is the same 'Top Suggestions' list Nike shows while typing, and it returns only search phrases with no product data. It omits rate limit and pagination details, keeping it short of a 5.

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?

One sentence, front-loaded with the core purpose and terminating in the output constraint. No filler sentences; every clause adds information.

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, read-only autocomplete tool with an output schema and safety annotations already in place, the description supplies everything an agent needs: the input is a partial string, the output is a flat phrase list, and no product data is returned. Nothing material 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?

There is a single parameter with 100% schema description coverage ('Partial search query'), so the schema already carries the semantics. The description's mention of 'partial query' reinforces but does not extend the schema, matching the baseline 3 for high coverage.

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 ('Nike's own search-box suggestions (typeahead) for a partial query') and pins down the output ('a flat list of suggested search phrases, no product data'). That 'no product data' clause implicitly distinguishes it from nike_search and nike_product, so an agent can route correctly without opening a 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 input description ('partial query') and the typeahead framing imply the use case, but the description never explicitly states when to prefer this over nike_search or nike_product_recommendations, nor any exclusions. Usage is inferable but not spelled out.

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