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everlane_search_suggest

Fetch Everlane predictive search suggestions for products, collections, and query terms by entering a search query. Set limits and types to retrieve relevant storefront results.

Instructions

Get Everlane search suggestions. Returns products, collections, and query suggestions from Everlane's (https://www.everlane.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

A3.5/5.0
Behavior3/5

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

No annotations, so the description bears the behavioral burden. It does disclose what is returned (products, collections, query suggestions) and that the storefront URL is fixed server-side, which is useful context. However it says nothing about rate limits, permissions, or error behavior.

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 purpose and return types; no wasted filler. Slightly more compact front-loading could push it higher.

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 3-param, fully documented search-suggest tool with no output schema, the description covers purpose, return shape, and the fixed-storefront detail. Adequate for an agent to call 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 q, limit, and types are already documented in the schema. The description adds no syntax or format details beyond that, making the baseline 3 appropriate.

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?

Specific verb+resource: 'Get Everlane search suggestions' and enumerates the return types (products, collections, query suggestions). It defines the capability clearly, though it doesn't distinguish itself from sibling suggest tools beyond the Everlane prefix.

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?

Usage is implied by being a predictive/autocomplete search tool, but there is no explicit guidance on when to use this versus everlane_products or other Everlane search endpoints, and no prerequisites or exclusions stated.

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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