🗺️ Google Maps Contributor Reviews API: a reviewer's history as clean JSON

The most efficient, reliable, and developer-friendly way to use the Google Maps Contributor Reviews API.

Actor page: apify.com/johnvc/google-maps-contributor-reviews-api Input schema: apify.com/johnvc/google-maps-contributor-reviews-api/input-schema

Pull a Google Maps contributor's review history as structured JSON. Give the API a contributor ID and get every review that reviewer has left, each with the rating, text, date, photos, and the place reviewed, plus the reviewer's own profile: Local Guide level, points, and total contributions. It turns a single reviewer into structured data for reputation research, reviewer vetting, and review-fraud detection.

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

   git clone https://github.com/johnisanerd/Google-Maps-Contributor-Reviews-API.git
   cd Google-Maps-Contributor-Reviews-API
   

2. Install dependencies with UV

   # 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

   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 an example

   # Single example:
   uv run python google-maps-contributor-reviews-api-example.py
   
   # Batch example (profiles several reviewers in one run):
   uv run python google-maps-contributor-reviews-api-batch-example.py
   

Alternative: set the API key directly

export APIFY_API_TOKEN="your_api_key_here"
uv run python google-maps-contributor-reviews-api-example.py

Why Use This Google Maps Contributor Reviews API?

Turn a reviewer into data. One contributor ID returns their full recent review history with the reviewer's profile attached.

Clean, structured output. Every review is one row, ready to load into a dataframe, a database, or an AI pipeline.

Built for fraud detection. The reviewer's level, points, and contribution counts, plus their review pattern across places, make mass reviewers and single-target campaigns easy to spot.

MCP-ready. AI agents can call it as a tool through the hosted Apify MCP server to profile a reviewer on demand.

Features

Core Capabilities

• A contributor's recent review history from one ID
• The reviewer's profile: level, points, local-guide status, contributions
• The place reviewed, with address and coordinates
• Batch several reviewers in one run

Data Quality

• One clean row per review, tagged with the contributor ID and profile
• Stable JSON shape, easy to load anywhere

Usage Examples

Basic Example

{
  "contributorId": "107022004965696773221"
}

Advanced Example

{
  "contributorIds": ["107022004965696773221", "100000000000000000000"],
  "maxResultsPerContributor": 10
}

For a runnable batch script, see google-maps-contributor-reviews-api-batch-example.py in this repo.

Input Parameters

Parameter	Type	Required	Default	Description
contributorId	str	one of	-	A single Google Maps contributor ID (the long numeric ID from a reviewer's profile).
contributorIds	list[str]	one of	-	A batch of contributor IDs. Merged with contributorId and de-duplicated.
hl	str	no	"en"	Two-letter language code.
maxResultsPerContributor	int	no	10	Reviews per contributor (about 10 are available per run).

Output Format

Each item in the dataset is one review, with the reviewer's profile attached:

{
  "result_type": "review",
  "contributor_id": "107022004965696773221",
  "position": 1,
  "review_id": "Ci9DQUlRQUNvZENodHljRjlvT21v...",
  "contributor_name": "Matt Moeini",
  "contributor_level": 5,
  "contributor_local_guide": true,
  "contributor_points": 952,
  "contributor_contributions": { "reviews": 32, "ratings": 1, "photos": 27, "videos": 7, "answers": 124 },
  "contributor_thumbnail": "https://lh3.googleusercontent.com/...",
  "rating": 5,
  "snippet": "Great little spot, the service was excellent ...",
  "date": "2 months ago",
  "likes": 0,
  "place_info": { "title": "Le Petit Marcel", "address": "2914 N Broadway, Chicago, IL 60657", "type": "Restaurant" },
  "images": [ { "title": "Le Petit Marcel", "thumbnail": "https://lh3.googleusercontent.com/..." } ],
  "details": { "food": 5, "service": 5, "atmosphere": 5, "recommended_dishes": "Salmon Wellington" },
  "link": "https://www.google.com/maps/...",
  "fetched_at": "2026-06-14T00:00:00Z"
}

Field reference

Field	Type	Description
result_type	str	Always review.
contributor_id	str	The reviewer this row belongs to.
position	int	Rank of this review within the reviewer's returned reviews.
review_id	str	Stable identifier for the review.
contributor_name	str	The reviewer's display name.
contributor_level	int	Local Guide level.
contributor_local_guide	bool	Whether the reviewer is a Local Guide.
contributor_points	int	The reviewer's Local Guide points.
contributor_contributions	obj	Counts: reviews, ratings, photos, videos, answers, and more.
contributor_thumbnail	str	The reviewer's profile photo URL.
rating	int	The star rating for this review.
snippet	str	The review text.
date	str	Relative date Google shows (e.g. 2 months ago).
likes	int	Likes on the review.
place_info	obj	The place reviewed: title, address, type, coordinates.
images	list	Photos attached to the review (title, thumbnail).
details	obj	Sub-ratings and tags when present (e.g. food, service, atmosphere, recommended_dishes).
response	obj	The business owner's response, when present.
link	str	Link to the review on Google Maps.
fetched_at	str	ISO 8601 timestamp of when the row was fetched.

Featured Tasks

Ready-to-run examples on the Apify Store, each targeting a specific use case:

• Get a Google Maps Reviewer's Full Review History
• Audit a Google Maps Local Guide's Reviews
• Export a Google Reviewer's Reviews as JSON
• Vet a Google Reviewer: Spot Fake-Review Signals

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<!-- The five install sections below are the canonical MCP install copy. -->

Install in Claude Cowork Desktop

Cowork is the desktop app's automation mode. To give it the Google Maps Contributor Reviews 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-maps-contributor-reviews-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 Maps Contributor Reviews 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

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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-maps-contributor-reviews-api"

To use a token instead of browser OAuth:

claude mcp add --transport http apify \
  "https://mcp.apify.com/?tools=actors,docs,johnvc/google-maps-contributor-reviews-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 Maps Contributor Reviews API.

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

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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-maps-contributor-reviews-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-maps-contributor-reviews-api, using OAuth when prompted.
5. Ask Claude to run the Google Maps Contributor Reviews API.

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

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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-maps-contributor-reviews-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-maps-contributor-reviews-api",
      "headers": { "Authorization": "Bearer YOUR_APIFY_TOKEN" }
    }
  }
}

3. Open Cursor → Settings → MCP and confirm the apify server is connected (green dot).
4. In Composer or Chat, ask Cursor to call the Google Maps Contributor Reviews API.

New to Cursor? Get it here: https://cursor.com/referral?code=XQP4VBLI3NNX

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Install in ChatGPT

ChatGPT connects to the Apify MCP server through Developer mode (available on ChatGPT Pro, Plus, Business, Enterprise, and Education plans).

1. Click your profile icon, then go to Settings > Apps. If you do not see a Create app button, open Advanced settings and enable Developer mode.
2. Click Create app and fill out the form:
   • Name: Apify
   • MCP Server URL: https://mcp.apify.com/?tools=actors,docs,johnvc/google-maps-contributor-reviews-api
   • Authentication: OAuth
3. Click Create and authorize the connection with Apify.
4. To use the app in a conversation, click + in the chat, choose Developer mode, and select Apify.

More help: https://docs.apify.com/platform/integrations/mcp

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Made with care

Use the Google Maps Contributor Reviews API to vet reviewers and detect review fraud in your product or AI agent.

Last Updated: 2026.06.14