# 🍴Uber Eats Scraper \[RENTAL] (`borderline/ubereats-scraper`) Actor

Rental - Fast and cost-effective Uber Eats Scraper requiring just a search query and location or by url. Instantly retrieve comprehensive restaurant data, including names, ratings, locations, reviews and menus. Seamlessly integrates with tools like n8n, and more, offering a highly efficient solution

- **URL**: https://apify.com/borderline/ubereats-scraper.md
- **Developed by:** [borderline](https://apify.com/borderline) (community)
- **Categories:** Lead generation, E-commerce, Automation
- **Stats:** 305 total users, 2 monthly users, 100.0% runs succeeded, 19 bookmarks
- **User rating**: 4.67 out of 5 stars

## Pricing

$35.00/month + usage

To use this Actor, you pay a monthly rental fee to the developer. The rent is subtracted from your prepaid usage every month after the free trial period. You also pay for the Apify platform usage, which gets cheaper the higher Apify subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#rental-actors

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## 🚀 Uber Eats Scraper Actor | Rental version

🎉 **Uber Eats Scraper** extracts comprehensive restaurant data from Uber Eats in two ways: **by address** (or address + query) or **by store URLs**. Perfect for gathering restaurant insights, menus, and pricing.

### ✨ Features

- **📍 Option A – By address or address + query**: Provide a delivery **address** (required). Optionally add a **query** (e.g. cuisine, restaurant name).

- **🔗 Option B – By URLs**: Provide a list of Uber Eats store page URLs to scrape those restaurants directly. When **urls** is not empty, **query** and **address** are ignored.

- **📊 Rich Detailed Data**: For each restaurant you get:
  - 🏠 **Restaurant Name & Sanitized Title**
  - ⭐ **Ratings & Customer Reviews**
  - 📍 **Complete Address (latitude, longitude)**
  - 🍕 **Full Menus & Pricing Details**
  - 🕑 **Opening Hours & Availability**
  - 📞 **Contact Information (Phone Number & Emails)**
  - 🖼️ **High-Quality Images (Logo & Hero Image)**
  - 📐 **Distance & Delivery ETA**
  - 🚧 **Open/Closed Status & Messages**

- **💻 Integrations**: Google Sheets, Slack, Zapier & Make.

***

### 🛠️ Input

Choose **one** of the two options below.

#### Option 1: 📍 Address (or address + query)

Provide a **delivery address** (required). You can use **address** alone or add an optional **query**:

```json
{
  "address": "1600 Pennsylvania Avenue NW, Washington",
  "query": "pizza",
  "locale": "en-US",
  "maxRows": 100
}
```

- **address** (required when not using urls): Delivery location (Address, City, Zip, etc.)
- **query** (optional): Cuisine type, restaurant name, or keyword (default: "Pizza")
- **locale**: Language/region (e.g. `en-US`, `fr-FR`)
- **maxRows**: Max restaurants to scrape (0 = all)
- **proxy** (optional): Proxy configuration. Residential proxy recommended for localized results.

#### Option 2: 🔗 Store URLs

Provide a list of **store page URLs**. When **urls** is not empty, **query** and **address** are ignored:

```json
{
  "urls": [
    "https://www.ubereats.com/fr/store/mcdonalds-paris-alesia/eHoH6Q_2RRikwI-xKk7qaQ"
  ],
  "locale": "fr-FR"
}
```

- **urls**: Array of Uber Eats restaurant/store page URLs. All given URLs are scraped (maxRows is ignored in this mode).
- **locale**: Used for API calls (e.g. `fr-FR` for French stores).
- **proxy** (optional): Proxy configuration. Residential proxy recommended.

***

### 🚀 Output Example (JSON)

The scraper returns detailed restaurant information, like:

```json
{
  "title": "NY Pizza Grill (DC)",
  "sanitizedTitle": "NY Pizza Grill (DC)",
  "emails": ["contact@mypizzagrill.com"],
  "phoneNumber": "+12025473838",
  "cuisineList": ["Italian", "Pizza", "American"],
  "location": {
    "address": "1442 Pennsylvania Ave SE, Washington, DC 20003",
    "streetAddress": "1442 Pennsylvania Ave SE",
    "city": "Washington",
    "country": "US",
    "postalCode": "20003",
    "region": "DC",
    "latitude": 38.8799987,
    "longitude": -76.9840023,
    "geo": {
      "city": "washington-dc",
      "country": "us",
      "neighborhood": "capitol-hill-washington-dc",
      "region": "dc"
    },
    "locationType": "PHYSICAL"
  },
  "currencyCode": "USD",
  "rating": { "ratingValue": 4.4, "reviewCount": "700+" },
  "storeAvailablityStatus": "AVAILABLE",
  "isOpen": true,
  "closedMessage": "",
  "etaRange": "Delivered in 35 to 55 min",
  "fareBadge": "Delivery Fee: $0.99",
  "hours": [
    {
      "dayRange": "Sunday - Thursday",
      "sectionHours": [{ "startTime": 630, "endTime": 60 }]
    },
    {
      "dayRange": "Friday - Saturday",
      "sectionHours": [{ "startTime": 630, "endTime": 150 }]
    }
  ],
  "categories": ["Italian", "Pizza", "American"],
  "categoriesLink": [
    { "text": "Italian", "link": "/us/category/washington-dc/italian" },
    { "text": "Pizza", "link": "/us/category/washington-dc/pizza" }
  ],
  "featuredItems": {
    "title": "Mozzarella Sticks",
    "itemDescription": "5 breaded mozzarella sticks with marinara.",
    "price": 999,
    "priceTagline": "$9.99",
    "isSoldOut": false,
    "isAvailable": true,
    "hasCustomizations": false,
    "endorsement": "Popular",
    "uuid": "b7ac95d8-11ea-4917-bc31-dd20c2407f29",
    "rating": "96%",
    "numRatings": 4,
    "featuredItems": "#1 most liked"
  },
  "menu": [
    {
      "catalogName": "Appetizers",
      "catalogSectionUUID": "71871c25-cb39-4375-b395-b95fe87f9edc",
      "catalogItems": [
        {
          "title": "Mozzarella Sticks",
          "itemDescription": "5 breaded mozzarella sticks with marinara.",
          "price": 999,
          "priceTagline": "$9.99",
          "isSoldOut": false,
          "isAvailable": true,
          "hasCustomizations": false,
          "endorsement": "Popular",
          "labelPrimary": "$9.99, 96% liked based on 4 reviews.",
          "uuid": "b7ac95d8-11ea-4917-bc31-dd20c2407f29",
          "rating": "96%",
          "numRatings": 4
        }
      ]
    }
  ],
  "logoImageUrl": "https://example.com/logo-image.jpeg",
  "heroImageUrl": "https://example.com/hero-image.jpeg",
  "distance": {
    "text": "1.1 KM",
    "accessibilityText": "1.1 kilometers"
  },
  "supportedDiningModes": ["DELIVERY", "PICKUP"],
  "storeReviews": [
    {
      "rating": 5,
      "text": "Great pizza and quick delivery!",
      "timeSinceReview": "1 week ago",
      "formattedDate": "March 1, 2025",
      "createdAt": "2025-03-01T13:45:00Z",
      "eaterName": "John D.",
      "contentUUID": "abcd-1234-efgh-5678"
    }
  ],
  "featuredReviews": [
    {
      "rating": 5,
      "text": "Best pizza in DC!",
      "timeSinceReview": "2 months ago",
      "formattedDate": "January 1, 2025",
      "createdAt": "2025-01-01T13:45:00Z",
      "eaterName": "Emily S.",
      "contentUUID": "ijkl-9876-mnop-5432"
    }
  ],
  "uuid": "bff91760-2d67-54d7-91db-9f6f875086a6",
  "url": "https://ubereats.com/us/store/ny-pizza-grill-dc/v_kXYC1nVNeR259vh1CGpg"
}
```

***

### 💻 Integrations & Automation

- **Google Sheets** 📊: Automatically export restaurant data for analysis.
- **Slack** 📩: Real-time restaurant data updates delivered instantly.
- **Zapier & Make** ⚡️: Connect effortlessly with thousands of apps for automated workflows.

***

### 📋 Example Use Cases

- **Competitive Analysis**: Quickly gather detailed competitor menus, prices, and ratings.
- **Data-Driven Marketing**: Efficiently collect restaurant data for targeted marketing campaigns.
- **Research & Development**: Accelerate product innovation by analyzing food trends and popularity insights.

***

### 💬 Support & Feedback

Need help or have questions? Please [open an issue](/issues) for dedicated support.

***

#### 🚀 **Start scraping effortlessly with Uber Eats Scraper today!**

# Actor input Schema

## `locale` (type: `string`):

Select locale (default is en-US). Applies to both search and URL mode.

## `address` (type: `string`):

Delivery address to search in. (ex: Address, City, Zip Code, etc.) Required when not using urls.

## `query` (type: `string`):

Query to search for. (ex: McDonald's, pizza, etc.) Optional when using address.

## `maxRows` (type: `integer`):

Maximum number of restaurants to scrape when using address/query. (0 = all). Not used in Option 2 (URLs): result count = number of input URLs.

## `urls` (type: `array`):

List of Uber Eats restaurant/store page URLs to scrape. When provided, address and query are ignored.

## `proxy` (type: `object`):

⚠️ Carefully select the proxy settings to ensure precise and localized search results. ⚠️

## Actor input object example

```json
{
  "locale": "en-US",
  "address": "71 Avenue Du Général Leclerc, Paris",
  "query": "Pizza",
  "urls": [
    "https://www.ubereats.com/fr/store/mcdonalds-paris-alesia/eHoH6Q_2RRikwI-xKk7qaQ"
  ],
  "proxy": {
    "useApifyProxy": true,
    "apifyProxyGroups": [
      "RESIDENTIAL"
    ]
  }
}
```

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "locale": "en-US",
    "address": "1600 Pennsylvania Avenue, Washington DC",
    "query": "Pizza",
    "maxRows": 0,
    "proxy": {
        "useApifyProxy": true,
        "apifyProxyGroups": [
            "RESIDENTIAL"
        ]
    }
};

// Run the Actor and wait for it to finish
const run = await client.actor("borderline/ubereats-scraper").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "locale": "en-US",
    "address": "1600 Pennsylvania Avenue, Washington DC",
    "query": "Pizza",
    "maxRows": 0,
    "proxy": {
        "useApifyProxy": True,
        "apifyProxyGroups": ["RESIDENTIAL"],
    },
}

# Run the Actor and wait for it to finish
run = client.actor("borderline/ubereats-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "locale": "en-US",
  "address": "1600 Pennsylvania Avenue, Washington DC",
  "query": "Pizza",
  "maxRows": 0,
  "proxy": {
    "useApifyProxy": true,
    "apifyProxyGroups": [
      "RESIDENTIAL"
    ]
  }
}' |
apify call borderline/ubereats-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,borderline/ubereats-scraper"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/rYHbrgLjSB53Eq3Q9/builds/D15aO76G31XvZBS6Y/openapi.json