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JavaScript
Remote

oldnavy_search

Search Old Navy, Gap, Banana Republic, or Athleta products by keyword to get normalized listings with pricing, review scores, and purchasable options.

Instructions

Search Old Navy, Gap, Banana Republic, or Athleta products. Searches product listings across Old Navy, Gap, Banana Republic, and Athleta -- select the storefront with the brand parameter (on for Old Navy, gap for Gap, br for Banana Republic, at for Athleta; defaults to on). Returns normalized product summaries with pricing, review scores, and every purchasable color variant. This search is best-effort relevance, not a guaranteed keyword match: for an obscure or nonsense keyword the upstream search index falls back to its own recommended results instead of returning an empty list, and there is currently no reliable signal in the response to distinguish a true keyword match from that fallback behavior.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoOne-based page
brandNoStorefront to search
keywordYesSearch keyword

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.17.5
    • addedInput schema / properties / brand / enum
      Added value: +[
+  "on",
+  "gap",
+  "br",
+  "at"
+]
  2. Addedv1.14.0

TDQS

A4.5/5.0
Behavior5/5

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

With no annotations provided, the description fully carries the burden of behavioral disclosure. It transparently reveals that search is not a guaranteed keyword matchيب fallback to recommended results for obscure keywords, and that there is no reliable signal to distinguish matches from fallback. It also details the return content (pricing, review scores, color variants). This is exemplary transparency.

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

Conciseness4/5

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

The description is front-loaded with the purpose and includes essential behavioral caveats Tar, but it is slightly redundant: the first two sentences list the same four brands twice. The fallback explanation is lengthy but necessary for correct use. Overall, it is reasonably concise given the complexity.

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?

Despite lacking an output schema, the description covers what is returned (normalized summaries with pricing, reviews, colors) and the critical fallback behavior. It does not mention pagination details beyond the schema's page parameter or error cases, but for a search tool these are minor gaps. The description is sufficiently complete for an agent to invoke it correctly.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3, but the description adds meaningful context beyond the schema: it explains the brand parameter's mapping to storefronts and the default value, and it clarifies that the keyword parameter behaves with best-effort relevance (fallback behavior). It does not add anything for the page parameter, but the schema already describes it. This extra semantics justifies a 4.

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 clearly states the tool searches products across four named brands (Old Navy, Gap, Banana Republic, Athleta), distinguishing it from other product-specific tools like oldnavy_product or oldnavy_product_recommendations. It specifies the verb (Search) and the resource (product listings), leaving no ambiguity about its purpose.

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?

It explicitly explains how to select the storefront via the brand parameter with valid values and defaults, providing clear context for use. It also warns about the best-effort relevance and fallback behavior, which helps the agent interpret results. However, it does not name alternative tools or explicitly state when not to use this tool, which would have earned 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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