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JavaScript
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prada_suggest

Get ranked Prada search suggestions with live result counts, then feed them into product searches. Returns an empty list when no matches exist.

Instructions

Get Prada search-box suggestions. Returns Prada's own search-box typeahead suggestions for a partial query -- a ranked list of suggested search phrases with the live result count each one would return. Pass a suggestion straight through to prada-search's own q parameter for product results. A query with no genuine matches returns a well-formed empty result rather than an error.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYesPartial search query
limitNoMaximum suggestions, defaults to 10, maximum 20

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.17.5

TDQS

A4.5/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden and handles it well. It discloses that results are ranked suggested phrases with live result counts, that queries with no matches return a well-formed empty result rather than an error, and that the source is Prada's own suggestion data.

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?

The description is compact and front-loaded: purpose, return shape, downstream usage, and edge-case behavior each appear in short, purposeful sentences. There is no filler or unnecessary schema restatement.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity, two well-documented parameters, and no output schema, the description is complete. It explains what the return data looks like, how to chain it into prada_search, and how no-match queries behave, giving an agent everything it needs 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?

The schema already documents both q and limit with 100% coverage, so the description does not need to repeat them. It adds the notion of a 'partial query' and notes the downstream use of suggestions, but it does not materially extend the schema's parameter descriptions.

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 Prada's search-box typeahead suggestions for a partial query. It clearly differentiates this from product search by noting that suggestions should be passed to prada-search's q parameter, which is a distinct sibling tool.

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 gives clear context for when this tool is appropriate—typeahead suggestions—and explicitly identifies prada_search as the downstream tool for product results. It does not explicitly state when to avoid using prada_suggest, so it stops short of a full when-not guidance.

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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