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Crawlora MCP

Crawlora MCP

Official
by Crawlora-org
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

wish_suggest

Fetch Wish search suggestions for a partial term and get a flat list of typeahead terms; no product data, and no matches returns an empty list.

Instructions

Get Wish search suggestions. Returns Wish's own search-suggestion (typeahead) result for a partial search term: a flat list of suggested search terms, no product data. A partial term with no matches returns a normal, empty result rather than an error.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "wish_suggest"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesPartial search term

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.14.0

TDQS

A4/5.0
Behavior4/5

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

With no annotations provided, the description shoulders the behavioral disclosure burden. It reveals the output shape (flat list, no product data) and the edge-case behavior for zero matches ('returns a normal, empty result rather than an error'). It does not cover rate limits, auth, or formatting, but these are less critical for a low-level suggest endpoint.

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 tight sentences, zero wasteful language. The first sentence delivers the core purpose, and the second adds a relevant edge case. It is front-loaded and easy to scan.

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 single-parameter tool with no annotations and no output schema, the description provides the input, return shape, and no-error edge case—enough to call it correctly. Its only noticeable gap is not explicitly directing the agent to wish_search for cases where product data is actually desired.

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 has 100% description coverage for the query parameter ('Partial search term'), so the baseline is 3. The description adds light extra meaning by placing the query in the typeahead context, but it doesn't materially expand 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?

The description opens with a clear verb and resource: 'Get Wish search suggestions.' It then specifies exactly what is returned—'Wish's own search-suggestion (typeahead) result' as a flat list of suggested search terms—and explicitly notes 'no product data,' which distinguishes it from product-search siblings like wish_search.

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 usage context is implied clearly: it is for partial search terms and typeahead suggestions. However, it never explicitly states when to prefer this over alternatives or when-not to use it, relying on the 'no product data' clause to rule out product lookup.

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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