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Decodo

Decodo MCP Server

by Decodo
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TypeScript
Remote

Google AI Mode

google_ai_mode
Read-only

Submit a query to Google AI Mode via Decodo's Web Scraping API to get its AI-generated answer as JSON.

Instructions

Ask Google AI Mode a question through the Decodo Web Scraping API (google_ai_mode target) and return its AI answer as JSON. Use it to see what Google's AI search says about a query. Docs: https://help.decodo.com/docs/web-scraping-api-google-ai-mode

Input Schema

NameRequiredDescriptionDefault
geoNoGeo location for AI mode search (e.g., "us", "uk")
queryYesSearch query for Google AI Mode (e.g., "What are the top three dog breeds?")
deviceTypeNoDevice type to emulate for the request

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.6.0
    • changedInput schema / properties / deviceType / enum
      Previous value: -[
-  "desktop",
-  "mobile",
-  "tablet"
-]New value: +[
+  "desktop",
+  "mobile"
+]
  2. First observed

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false, so the safety profile is covered. The description adds that the call goes through the Decodo Web Scraping API (implying proxied, possibly rate-limited/latency-bearing access) and that the result is JSON, plus a docs link, but says nothing about latency, quotas, or failure modes.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short sentences plus a docs link, with the action front-loaded. The parenthetical API target and URL are slightly incidental but useful; nothing is padded.

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

Completeness3/5

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

For a simple three-parameter read tool with no output schema, the description covers intent and return type ("AI answer as JSON") but leaves the response shape, sourcing behavior, and any limits unstated. Adequate but with visible gaps.

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%, and all three parameters (query, geo, deviceType) are already documented with examples and an enum in the schema. The description adds no parameter meaning beyond the schema, so the baseline 3 applies.

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 and resource: ask Google AI Mode a question via Decodo and return the AI answer as JSON. It's clear what it does, but it doesn't distinguish itself from close siblings like google_search, chatgpt, or perplexity.

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?

"Use it to see what Google's AI search says about a query" gives an implied usage context, but there is no guidance on when to prefer this over google_search or chatgpt/perplexity, and no exclusions.

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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