🔎 Google AI Overview API: Get Google's AI answer and its sources as JSON

The most efficient, reliable, and developer-friendly way to use the Google AI Overview API.

Actor page: apify.com/johnvc/Google-AI-Overview-API Input schema: apify.com/johnvc/Google-AI-Overview-API/input-schema

Send any search query and get back Google's AI Overview, the AI-generated answer that appears at the top of Google's results, as structured JSON: the answer text blocks plus the sources it cites (title, link, snippet, and source name). When Google defers generation and returns only a short-lived token, this API resolves the full answer for you automatically, so you always get clean, ready-to-use data.

Video Walkthrough

Quick Start

Prerequisites

• Python 3.11 or higher
• An Apify account and API key (get a free key here)

1. Clone the repository

   bash

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   git clone https://github.com/johnisanerd/Apify-Google-AI-Overview-API.git
   cd Apify-Google-AI-Overview-API
   

2. Install dependencies with UV

   bash

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   # Install UV if you do not have it:
   curl -LsSf https://astral.sh/uv/install.sh | sh
   
   # Install project dependencies:
   uv sync
   

3. Configure your API key

   bash

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   cp .env.example .env
   # Edit .env and add your Apify API key
   # Get your free API key at: https://apify.com?fpr=9n7kx3
   

4. Run the example

   bash

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   uv run python google-ai-overview-api-example.py
   

Alternative: set the API key directly

export APIFY_API_TOKEN="your_api_key_here"
uv run python google-ai-overview-api-example.py

Why Use This Google AI Overview API?

Just the answer, structured. You get the AI Overview content and its cited sources as clean JSON, ready to drop into a database, a report, or an LLM pipeline. No HTML parsing.

It handles Google's deferral for you. Google sometimes returns the AI Overview immediately and sometimes defers it behind a short-lived token. This API resolves that follow-up automatically in a single run, so you never have to manage tokens that expire in under a minute.

One query or many. Pass a single query or a queries list. You get one result row per query, which makes batch lookups and GEO/AEO monitoring straightforward.

Pay only for what you fetch. Pricing is per retrieval: one retrieval for a standard inline answer, two only when Google defers and a follow-up is required. The output tells you exactly how many were used.

Country and language targeting. Choose the country (gl) and language (hl), or narrow to a specific location.

MCP-ready. Load it as a tool in Claude Cowork, Claude Code, Claude on the web, and Cursor (see below) and ask for an AI Overview in plain language.

Features

Core Capabilities

• Returns the AI Overview answer as ordered text_blocks (paragraphs, headings, lists)
• Returns the cited references for each answer (title, link, snippet, source)
• Automatic resolution of Google's deferred (token-based) generation
• Single-query and batch (queries) input, one row per query
• Country, language, and location targeting

Data Quality

• Clean, typed JSON output, one row per query, with a stable result_type field
• A clear ai_overview_present flag and a note when Google shows no overview
• Transparent retrievals_used count so cost is never a surprise

Usage Examples

Basic Example

{
  "query": "what is the Siliconimist podcast?"
}

Advanced Example

{
  "queries": [
    "best practices for prompt engineering",
    "how does vector search work"
  ],
  "gl": "us",
  "hl": "en",
  "location": "Austin, Texas, United States"
}

Input Parameters

At least one of query or queries is required.

Output Format

One row per query. Each item has a result_type of ai_overview or error. The answer text and sources below are illustrative; your run returns the live AI Overview content and its real citations.

{
  "result_type": "ai_overview",
  "query": "what is retrieval augmented generation",
  "ai_overview_present": true,
  "used_dedicated_api": false,
  "retrievals_used": 1,
  "text_blocks": [
    {
      "type": "paragraph",
      "snippet": "<AI Overview answer paragraph for the query>"
    }
  ],
  "references": [
    {
      "title": "<cited source title>",
      "link": "https://www.example.com/source",
      "snippet": "<short snippet from the cited source>",
      "source": "Example",
      "index": 1
    }
  ],
  "gl": "us",
  "hl": "en",
  "fetched_at": "2026-05-28T08:33:58.988871+00:00"
}

When Google does not show an AI Overview for a query, the row comes back with "ai_overview_present": false and a short note explaining why.

Use as an MCP tool

You can load the Google AI Overview API as an MCP tool so assistants call it for you. The MCP server URL preloads just this one Actor:

https://mcp.apify.com/?tools=actors,docs,johnvc/Google-AI-Overview-API

Authenticate with OAuth in the browser when offered, or with your Apify API token (the same APIFY_API_TOKEN used by the Python example). Get a token at https://console.apify.com/settings/integrations and a free Apify account at https://apify.com?fpr=9n7kx3 .

Install in Claude Cowork Desktop

Cowork is the desktop app's automation mode. To give it the Google AI Overview API as a tool, add the Apify MCP server as a connector.

1. Open the Claude desktop app and go to Settings → Connectors (or Settings → Developer → Edit Config to edit claude_desktop_config.json directly).
   • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
   • Windows: %APPDATA%\Claude\claude_desktop_config.json
2. Add the Apify MCP server, preloaded with only this Actor:

{
  "mcpServers": {
    "apify": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://mcp.apify.com/?tools=actors,docs,johnvc/Google-AI-Overview-API"
      ]
    }
  }
}

3. Restart the app. When Cowork first calls the tool, complete the OAuth prompt in your browser, or add your Apify API token in the connector settings to skip OAuth.
4. In a Cowork chat, confirm the tool is available and ask it to run the Google AI Overview API.

Download the desktop app and start a free trial: https://claude.ai/referral/uIlpa7nPLg More help: https://docs.apify.com/platform/integrations/claude-desktop

Install in Claude Code

Claude Code is the command-line tool. Add the Actor's MCP server with one command:

claude mcp add --transport http apify \
  "https://mcp.apify.com/?tools=actors,docs,johnvc/Google-AI-Overview-API"

To use a token instead of browser OAuth:

claude mcp add --transport http apify \
  "https://mcp.apify.com/?tools=actors,docs,johnvc/Google-AI-Overview-API" \
  --header "Authorization: Bearer YOUR_APIFY_TOKEN"

Then verify with claude mcp list, or run /mcp inside a session. Ask Claude Code to call the Google AI Overview API.

Try Claude Code free: https://claude.ai/referral/uIlpa7nPLg Claude Code MCP docs: https://code.claude.com/docs/en/mcp

Install in Claude (website)

On claude.ai you add Apify as a connector, then enable just this Actor's tool.

1. Go to Settings → Connectors → Browse connectors and search for Apify MCP server. Install it (enable or update if prompted).
2. When connecting, authenticate with your Apify API token, and enable the tool johnvc/Google-AI-Overview-API.
3. In any chat, open + → Connectors and turn on Apify.
4. Alternatively, choose Add custom connector and paste the full MCP URL https://mcp.apify.com/?tools=actors,docs,johnvc/Google-AI-Overview-API, using OAuth when prompted.
5. Ask Claude to run the Google AI Overview API.

Open Claude on the web: https://claude.ai/referral/uIlpa7nPLg

Install in Cursor

Cursor reads MCP servers from a project file at .cursor/mcp.json.

1. In your project, create .cursor/mcp.json:

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com/?tools=actors,docs,johnvc/Google-AI-Overview-API"
    }
  }
}

2. If you prefer token auth over browser OAuth, add a header:

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com/?tools=actors,docs,johnvc/Google-AI-Overview-API",
      "headers": { "Authorization": "Bearer YOUR_APIFY_TOKEN" }
    }
  }
}