# Extract Restaurant Food, Service & Atmosphere Ratings

**Use case:** 

Scrape a restaurant's Google reviews and capture the review_details field, which breaks out per-service ratings like Food, Service and Atmosphere when reviewers provide them. Pair it with review_text and author_rating to see exactly what drives each score. Note per-service ratings only appear when the reviewer filled them in, so some reviews will have none.

## Input

```json
{
  "Urls": [
    "https://www.google.com/maps/place/CAVA/data=!4m5!3m4!1s0x89c25b1573b6d42b:0x544335f45362df10!8m2!3d40.6926226!4d-73.9884258?authuser=0&hl=en&rclk=1"
  ],
  "maxCrawlPages": 40,
  "language": "English (United States)",
  "sort_by": "Most relevant",
  "reviewsStartDate": "2026-01-01"
}
```

## Output

```json
{
  "shop_name": {
    "label": "Shop Name",
    "format": "text"
  },
  "author_name": {
    "label": "Reviewer",
    "format": "text"
  },
  "author_rating": {
    "label": "Rating",
    "format": "number"
  },
  "review_datetime": {
    "label": "Review Date",
    "format": "text"
  },
  "review_text": {
    "label": "Review",
    "format": "text"
  },
  "review_details": {
    "label": "Review Details",
    "format": "object"
  },
  "review_image_urls": {
    "label": "Review Images",
    "format": "array"
  },
  "is_local_guide": {
    "label": "Local Guide",
    "format": "boolean"
  },
  "review_language": {
    "label": "Language",
    "format": "text"
  },
  "response_from_owner_text": {
    "label": "Owner Response",
    "format": "text"
  },
  "response_from_owner_date": {
    "label": "Owner Response Date",
    "format": "text"
  },
  "is_edited": {
    "label": "Edited",
    "format": "boolean"
  },
  "review_timestamp": {
    "label": "Review Date (relative)",
    "format": "text"
  },
  "author_review_count": {
    "label": "Reviewer Total Reviews",
    "format": "number"
  },
  "author_photo_count": {
    "label": "Reviewer Total Photos",
    "format": "number"
  },
  "author_profile_link": {
    "label": "Reviewer Profile",
    "format": "link"
  },
  "author_photo_url": {
    "label": "Reviewer Photo",
    "format": "image"
  },
  "shop_rating": {
    "label": "Shop Rating",
    "format": "number"
  },
  "shop_review_count": {
    "label": "Shop Total Reviews",
    "format": "number"
  },
  "shop_review_tags": {
    "label": "Shop Review Keywords",
    "format": "object"
  },
  "review_link": {
    "label": "Review Link",
    "format": "link"
  },
  "review_id": {
    "label": "Review ID",
    "format": "text"
  },
  "input_url": {
    "label": "Input URL",
    "format": "link"
  }
}
```

## About this Actor

This example demonstrates how to use [Google Maps Reviews Scraper - Text, Ratings & Exact Dates](https://apify.com/delicious_zebu/google-maps-store-review-scraper.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/delicious_zebu/google-maps-store-review-scraper.md) to learn more, explore other use cases, and run it yourself.


## How to integrate an Actor?

This Task's input is already configured above. Use it as-is rather than inventing a new one.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For full API examples (JavaScript, Python, CLI, MCP, OpenAPI), see this Task's Actor page: https://apify.com/delicious_zebu/google-maps-store-review-scraper.md

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).