Skip to main content
Glama
Sign In
Crawlora MCP

Crawlora MCP

Official
by Crawlora-org
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

depop_suggest

Get Depop search-box autocomplete suggestions for partial queries, including mapped categories, to power accurate search inputs and filters.

Instructions

Depop search-box autocomplete. Returns Depop's own search-box autocomplete suggestions for a partial query, including the category a suggestion maps to when relevant. Public data sourced from Depop's own search-suggestions API.

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": "depop_suggest"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesPartial search query to autocomplete

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.14.0

TDQS

A4.2/5.0
Behavior3/5

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

No annotations are present, so the description must carry the full behavioral burden. It discloses that results are public data from Depop's own API and that category mapping is included where relevant. However, it doesn't mention whether suggestions are limited, rate-limited, or shaped differently from the native Depop UI, leaving some behavioral ambiguity.

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, front-loaded with the purpose, and provides the additional API source detail in a second sentence. There is no filler or redundancy.

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 one-parameter autocomplete tool, the description covers the core use case and clarifies the output type (suggestions) with optional category mapping. It doesn't describe exact output shape or limits, but that is less critical given the absence of an output schema is common for lightweight suggestion tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema covers the single 'query' parameter with a clear description. The tool description also clarifies the parameter's role as a partial query, which supports the schema without contradicting. With 100% schema coverage and a single parameter, the description adds enough context to move slightly above baseline.

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 clearly states the tool is Depop's search-box autocomplete, returning suggestions for a partial query. It also includes the relevant detail that category mapping is returned where applicable, which differentiates it as a lightweight autocomplete rather than a full search 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 clearly indicates when to use it (for partial query autocomplete) and specifies the input is 'partial'. It doesn't explicitly contrast with sibling tools like depop_search or amazonsuggest, but the context is sufficient for an agent to select the right operation for typeahead behavior.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools