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zappos_brands

List Zappos brands from the published sitemap to find brand IDs or URLs, then pair them with the brand endpoint to browse product grids. Filter results by name or slug.

Instructions

List Zappos brands. Returns a page of Zappos brands discovered from Zappos's own published sitemap, closing the discovery gap where a brand id otherwise has to be found elsewhere. Pair a returned id or url with GET /zappos/brand to browse that brand's product grid. name is derived from the brand's own URL slug (title-cased), not an authoritative site-provided label. q, if set, case-insensitively filters to brands whose derived name or slug contains it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoCase-insensitive substring filter on name or slug
pageNoResult page, 1-based, defaults to 1
page_sizeNoResults per page, defaults to 100, max 1000

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.14.0

TDQS

A4.2/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the data source (Zappos's published sitemap), that names are derived from URLs rather than authoritative labels, and that q is case-insensitive. It doesn't mention rate limits or pagination edge cases, but for a read-only list endpoint that is a minor gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Each of the four sentences has a distinct informational role: purpose, data source and discovery gap, next-step pairing, and naming caveat/filter semantics. No sentence is wasted and the main purpose is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description tells the agent what the tool returns (a page of brands with ids/URLs), how to use those in the next step, and data quality caveats. With no output schema, it provides enough context about returned ids and names, though a structured description of the response payload would be even more complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the parameters q/page/page_size are already fully documented in the input schema. The description restates q's filtering behavior without adding meaningful new semantics beyond the schema coverage. Baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'List Zappos brands,' which states the exact action and resource. It also distinguishes the tool's role from zappos_brand by explaining that this endpoint closes the discovery gap when a brand id isn't known, and that one should 'Pair a returned id or url with GET /zappos/brand' for browsing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly signals when to use this discovery tool vs. the specific brand retrieval endpoint. It frames the gap ('otherwise has to be found elsewhere') and routes the user onward to the brand detail tool. It doesn't provide an explicit 'do not use when...' list, so it falls just shy of a 5.

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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