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JavaScript
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google_suggest

Generate Google autosuggest query completions for web, YouTube, or shopping via Crawlora MCP, with optional type, relevance score, and description.

Instructions

Suggest Google search queries. Returns Google autosuggest query completions from the public unauthenticated suggest JSON endpoint. source selects the web, YouTube, or shopping suggestion list, and rich=true adds a type, relevance score, and short description to each suggestion.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYesSearch query prefix
langNoGoogle UI language; defaults to en
richNoAdd Google's type, relevance score, and description to each suggestion; defaults to false
countNoSuggestions to return; defaults to 10, clamped to 1..12
sourceNoSuggestion source; defaults to web
countryNoGoogle result country; defaults to us

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.17.9
    • addedInput schema / properties / rich
      Added value: +{
+  "description": "Add Google's type, relevance score, and description to each suggestion; defaults to false",
+  "type": "boolean"
+}
    • addedInput schema / properties / source
      Added value: +{
+  "description": "Suggestion source; defaults to web",
+  "enum": [
+    "web",
+    "youtube",
+    "shopping"
+  ],
+  "type": "string"
+}
  2. First observedv1.0.0

TDQS

A3.8/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden, and it adds real value: it discloses that the endpoint is public and unauthenticated (no credentials needed) and that rich=true enriches each suggestion with a type, relevance score, and short description. It stops short of describing rate limits, error behavior, or stability guarantees for a scrape-sourced 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 front-loaded sentences with zero filler: the first states what it returns and where from, the second covers the two most behaviorally meaningful parameters. No sentence is wasted.

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?

With no annotations and no output schema, the description needs to carry behavior, and it does reasonably well for a read-only suggestion tool: it explains the data origin, the source variants, and the rich output shape. Minor gaps remain (rich=false baseline shape, count clamping is only in the schema) but nothing an agent needs to invoke it correctly 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 every parameter is already documented in the schema and the baseline is 3. The description reinforces the meaning of `source` (web/YouTube/shopping) and `rich` (adds type, relevance, description) but adds no syntax or format detail beyond what the schema already states.

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?

States a specific verb (Suggest) and resource (Google search queries) and specifies it returns autosuggest completions. It is clearly distinguishable from near-siblings like youtube_suggest or yahoo_search_suggest by naming the Google autosuggest source, though it never explicitly contrasts itself with those alternatives.

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 the purpose (feed a query prefix to get completions), but there is no explicit when-to-use/when-not guidance nor any routing to alternatives such as the many other *_suggest tools in the catalog. The `source` enum hint is the closest thing to selection 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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