search_sneakersSearches the HYPEIBIZA catalogue for sneakers and streetwear by name, model or style code and returns up to 10 products with the lowest shop price.
| Name | Required | Description | Default |
|---|---|---|---|
| size | No | Only products a shop has in stock in this size; the price is then the lowest price in that size. | |
| limit | No | ||
| query | Yes | A product name, model, brand or style code, for example 'jordan 1 low' or 'DD1391-100'. |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and non-destructive behavior, so the description's added value is in the return semantics: a cap of 10 results ranked by lowest shop price. That ranking/limit detail is genuine behavioral context beyond the annotations, though it omits pagination or empty-result behavior.
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?
A single front-loaded sentence that packs the resource, match fields, result cap, and pricing rule with no filler. Slightly long but every clause carries 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?
No output schema exists, and the description compensates by stating the return shape (up to 10 products) and ranking criterion (lowest shop price). Only minor gaps remain, such as how few-result or no-result cases behave.
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 67% and the query parameter is already well documented with examples in the schema; the description restates the same matching fields. The size-driven pricing nuance and the limit cap are only partly reflected, so with the schema doing most of the work a baseline 3 is appropriate.
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 (searches) and resource (HYPEIBIZA catalogue for sneakers and streetwear) plus the fields matched (name, model, style code). It is clearly distinct from convert_size and get_deals, though it does not explicitly distinguish itself from the overlapping get_offers sibling.
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 implies usage for product lookup by name/model/style code, but never states when to prefer this over get_offers or get_deals, nor any exclusions or prerequisites. The agent must infer the boundary from the sibling names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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