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Google Search MCP Server

Google Search MCP Server

by HasData
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

google_serp_ai_overview: GET /

hasdata_google_serp_ai_overview_getAiOverviewResponse

Fetch Google's AI Overview using a page token from the SERP API. Returns AI answer text, source URLs, and subtopic sections for tracking citations, fact-checking, and LLM retrieval.

Instructions

Get AI Overview Results

Fetches the lazy-loaded Google AI Overview block via a pageToken returned by the Google SERP API (token valid for 4 minutes). Returns the AI-generated answer text, referenced source URLs, and expanded subtopic sections. Use as a follow-up call to Google SERP for tracking AI citations in SEO, fact-checking answers against sources, and LLM retrieval pipelines grounded in live Google results.

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": {
      "pageToken": "<pageToken>"
    },
    "name": "hasdata_google_serp_ai_overview_getAiOverviewResponse"
  }
}

Input Schema

NameRequiredDescriptionDefault
pageTokenYesToken from `aiOverview` block in Google SERP API. Valid for 4 minutes.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses that the block is lazy-loaded, the token is valid for only 4 minutes, and it returns specific content. While it doesn't explicitly state read-only behavior, the description implies a safe fetch operation.

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 concise and well-structured: a clear title line, a brief explanation of what is fetched, the returned contents, and explicit use cases. No redundant or extraneous text.

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?

Given the simple input (one parameter, no nested objects) and no output schema, the description is complete. It explains the purpose, parameter source and validity, expected return contents, and appropriate use cases, giving the agent everything needed to decide and invoke correctly.

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?

The sole parameter, pageToken, is fully described in the input schema ('Token from aiOverview block... Valid for 4 minutes'), and the tool description repeats this same information without adding new meaning. Since schema coverage is 100%, baseline is 3.

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's action ('Get AI Overview Results') and resource (Google AI Overview block), distinguishes it from sibling tools by specifying it's a follow-up using a pageToken, and details the returned data (answer text, source URLs, subtopic sections).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly provides use cases: 'tracking AI citations in SEO, fact-checking answers against sources, and LLM retrieval pipelines grounded in live Google results.' It also implies the prerequisite of having a pageToken from a prior SERP call, making it clear when this tool is appropriate.

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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