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JavaScript
Remote

fashionnova_search_suggest

Fetch Fashion Nova search suggestions, including products, collections, and queries, from its credential-free predictive search endpoint.

Instructions

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

Input Schema

TableJSON 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

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses the endpoint is 'credential-free' (no auth needed) and that the storefront URL is 'fixed server-side' (no URL configuration required). It also names the three response categories. It doesn't cover rate limits or exact response shape, but the key behavioral constraints are stated.

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?

Three short sentences, all purposeful: the first states the purpose, the second gives the source and return types, and the third clarifies server-side configuration. No filler or redundancy.

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, combined with the schema, covers what is returned, the acceptable parameter values, and the auth/configuration context. There is no output schema, so the exact JSON structure is unspecified, but for a lightweight suggest endpoint the agent has enough to select and invoke 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 coverage is 100%: q, limit, and types each have descriptions. The description adds a hint by listing the return types, but it doesn't explain parameter semantics beyond what the schema already provides. This matches the baseline for high schema coverage.

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 states a specific verb and resource: 'Get Fashion Nova search suggestions,' and clarifies the return categories: 'products, collections, and query suggestions.' This distinguishes it from sibling tools like fashionnova_products and fashionnova_collections, which retrieve full records rather than suggestions.

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 makes the use case clear: it is for search suggestions from Fashion Nova's predictive search endpoint. It doesn't explicitly name alternatives or say when not to use it, but the distinction from full product/collection search tools is evident from the 'suggestions' framing and the enumerated return types.

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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