Crawlora MCP

thebodyshop_search_suggest

Read-only

Fetch The Body Shop predictive-search suggestions for an incomplete query, including product, collection, and query matches. Use thebodyshop_products to browse the catalog or thebodyshop_collections for collection listings.

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": {
      "q": "<q>"
    },
    "name": "thebodyshop_search_suggest"
  }
}

Input Schema

Name	Required	Description
`q`	Yes	Required. Search query text.
`limit`	No	Maximum suggestions to return. Default 10, maximum 20.
`types`	No	Comma-separated suggestion types. Allowed values: product, collection, query. Defaults to product,collection,query.

Output Schema

Name	Required	Description	Default
`data`	Yes	The tool result payload (shape varies per tool; see each tool's docs resource).

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 5, 2026

TDQS

A4/5.0

Behavior3/5

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

Annotations already declare readOnlyHint and openWorldHint, so the safety profile is covered. The description adds that the tool is predictive/typeahead oriented and that output spans product/collection/query match kinds - useful context - but says nothing about return volume or pagination behavior beyond what the annotations and output schema imply.

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 sentences, front-loaded with the core purpose and immediately followed by the sibling routing. No filler and nothing that repeats structured fields verbatim.

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 low-complexity three-parameter suggestion tool with an output schema, the description covers purpose, trigger, and alternatives adequately. Return values need not be explained given the output schema, so only minor behavioral detail is missing.

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 limits and the types enum values are already fully documented in the schema. The description's mention of 'product, collection, and query matches' loosely mirrors the types parameter but adds no syntax or default detail 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?

Specific verb ('Fetch') plus resource ('The Body Shop predictive-search suggestions') and scope ('for an incomplete query'). It also names the sibling alternatives (thebodyshop_products, thebodyshop_collections), so an agent can distinguish it from catalog/list tools without reading their schemas.

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?

States the trigger condition ('for an incomplete query') and explicitly routes the agent to alternatives for the full-catalog and collection use cases. No explicit when-not-to-use statement, but the alternative routing is clear enough to guide selection.

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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