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skims_search_suggest

Fetch SKIMS predictive search suggestions by query, returning matching products, collections, and query terms for autocomplete or discovery.

Instructions

Get SKIMS search suggestions. Returns products, collections, and query suggestions from SKIMS's (https://skims.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

A3.6/5.0
Behavior3/5

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

With no annotations, the description must carry the behavioral load. It does disclose that results are prediction-based (products, collections, query strings) and that the storefront URL is fixed server-side, meaning no store parameter is needed. It says nothing about authentication, rate limits, or result ordering, so it is helpful but incomplete 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.

Conciseness5/5

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

Two short sentences with zero filler; the primary purpose is front-loaded and the server-side URL note is a compact, relevant addendum.

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 read-only suggestion endpoint with a fully documented three-parameter schema and no output schema, the description covers what the tool does and what kinds of results come back. Only minor gaps remain (ordering, whether results are truncated per type), which are not critical for correct invocation.

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 (default 10, max 20), and types. The description's mention of "products, collections, and query suggestions" loosely maps to the allowed types values but adds no new syntax or constraint details beyond the schema, so the baseline 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 SKIMS search suggestions") and enumerates the return categories (products, collections, query suggestions) from SKIMS's predictive search. It clearly identifies the SKIMS storefront, which distinguishes it from the many other brand suggest tools by name, though it does not explicitly contrast with siblings like skims_products.

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

Usage Guidelines3/5

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

The phrase "predictive search" implies the typeahead/autocomplete use case, which gives an agent enough to infer when this tool applies. However, there is no explicit when-to-use guidance, no exclusions, and no reference to alternatives such as skims_products or skims_collections for full catalog search.

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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