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fashionnova_search_suggest

Retrieve Fashion Nova predictive search suggestions, including products, collections, and queries, to support storefront search autocomplete and discovery.

Instructions

Get Fashion Nova search suggestions. Returns products, collections, and query suggestions from Fashion Nova's (https://www.fashionnova.com) predictive search. The storefront URL is fixed server-side.

Input Schema

NameRequiredDescriptionDefault
qYesSearch query
limitNoMaximum results per type, defaults to 10 and supports up to 20
typesNoComma-separated suggestion types. Allowed values: product, collection, query

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.16.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It does disclose that the storefront URL is fixed server-side (i.e., no URL parameter is accepted or configurable) and enumerates the return categories, which is useful. However, it says nothing about rate limits, auth requirements, or result ordering, leaving meaningful gaps for a no-annotation tool.

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?

Three short sentences, front-loaded with the core purpose and the source domain. Content is efficient, though the URL parenthetical and the 'fixed server-side' note are secondary details that could be trimmed.

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?

For a low-complexity suggestion tool with a fully documented 3-parameter schema and no output schema, the description adequately covers what the tool returns and its data source. The main missing piece is usage routing against sibling fashionnova tools, but overall it is sufficient to call the tool correctly.

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 schema already documents q, limit, and types with formats and allowed values. The description only echoes the return categories that map loosely to the types parameter and adds no syntax or constraint detail beyond the schema, so the baseline of 3 applies.

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

Purpose4/5

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

States a specific verb and resource ('Get Fashion Nova search suggestions') and clarifies it comes from the site's 'predictive search', which is a meaningful distinction from an explicit search tool. It describes the returned content types (products, collections, query suggestions), making the purpose clear, though it does not explicitly differentiate itself from siblings like fashionnova_products or the many other *_search_suggest tools.

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

Usage Guidelines2/5

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

The description only describes what is returned; it never states when to choose this tool over alternatives (e.g., fashionnova_products, fashionnova_collections) or when it should not be used. The phrase 'predictive search' weakly implies an autocomplete use-case, but no explicit guidance is given.

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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