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Google Search Remote MCP Server

by com.hasdata
OverviewDiscussionsAdmin

google_serp_ai_mode: GET /

hasdata_google_serp_ai_mode_getAiModeResponse

Get AI Mode SERP Results

Captures Gemini-powered AI Mode answers from Google Search. Returns the conversational response text, cited source links, subtopic breakdowns, follow-up suggestions, and a subsequentRequestToken for multi-turn continuation. Use for next-gen search interfaces, AI-answer monitoring, citation tracking, content research agents, building question-answering pipelines grounded in live Google results, and person/company data enrichment — e.g. asking Who is the CEO of HasData?, What is Roman Milyushkevich's LinkedIn?, HasData founder email, HasData Instagram handle to get a synthesized answer plus source URLs in one call, ideal for lead enrichment, sales research, people search, and filling in contact/attribute gaps for CRM records.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYesSpecify the search term for which you want to scrape the SERP.
glNoThe two-letter country code for the country you want to limit the search to. Provide one exact documented value (245 allowed), e.g. `ac`, `af`.
hlNoThe two-letter language code for the language you want to use for the search. Provide one exact documented value (159 allowed), e.g. `af`, `ak`.
uuleNoThe encoded location parameter.
locationNoGoogle canonical location for the search.
continuableNoWhether to continue an existing AI Mode conversation.
subsequentRequestTokenNoToken used to continue a previous AI Mode request.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

No annotations provided, but the description clearly explains the output and the continuation mechanism, making behavior transparent.

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?

The description is detailed and informative, though slightly verbose with repeated examples; it is still well-structured and front-loaded.

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

Completeness5/5

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

The description fully explains what the tool does, its output, and its multi-turn continuation feature, making it complete for the given complexity.

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 covers all parameters with descriptions; the tool description does not add additional meaning beyond the schema, so a neutral score.

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?

Clearly states it gets AI Mode SERP results from Google, distinguishing from other Google SERP tools like AI Overview.

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?

Provides explicit use cases and examples, but does not explicitly compare with alternative tools, so slightly less than perfect.

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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