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JavaScript
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moncler_suggest

Fetch Moncler search-box suggestions for a partial query, including completed or corrected terms, suggested phrases, and matching categories. Returns typeahead text, not product data.

Instructions

Get Moncler search-box suggestions. Returns the storefront's own search-box suggestions (typeahead) for a partial query: completed/corrected search terms, suggested full phrases, and matching categories. Not product data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYesPartial search query

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.17.5

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden. It discloses that this returns the storefront's own search-box suggestions and clarifies it is not product data, implying a read-only operation. It does not address edge cases like empty queries, result limits, or error behavior, so it is adequate but not rich.

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?

Two sentences, front-loaded with the core purpose, followed by a precise list of return types and an exclusion. No wasted words.

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 simple one-parameter suggest tool, the description covers purpose, input type, and output categories, and explicitly disambiguates from product data. It lacks output structure details (array vs object, limits), but the absence of an output schema makes this a minor gap.

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% for the single parameter q ('Partial search query'), and the description reinforces this by calling it a partial query. The description adds context about what the suggestions contain but does not add new parameter-specific details beyond the schema.

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?

States a specific verb 'Get' and resource 'Moncler search-box suggestions', and enumerates the returned content (completed/corrected terms, full phrases, matching categories). The explicit 'Not product data' distinguishes it from product-focused siblings like moncler_product and moncler_search.

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?

Clearly indicates this is for partial-query typeahead suggestions and explicitly excludes product data, giving an agent a sense of when to use it. However, it does not name alternative tools for full search or product lookup, so there is no explicit when-not-to-use beyond the 'not product data' note.

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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