# Ubereats Stores Discovery By Brand URL (`datacach/ubereats-stores-discovery-by-brand-url`) Actor

Easily extract all Uber Eats store URLs for any brand (McDonald's, Starbucks, Subway, etc.) at scale. This tool converts a list of brand profile links into a JSON feed with direct URLs for each restaurant. Ideal for local analysis, research, and franchise mapping.

- **URL**: https://apify.com/datacach/ubereats-stores-discovery-by-brand-url.md
- **Developed by:** [DataCach](https://apify.com/datacach) (community)
- **Categories:** Automation, Lead generation, E-commerce
- **Stats:** 6 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$2.99 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

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

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

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 examples already wired to this Actor's own input schema, see the [API](#api) section below.

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`).

# README

## 🚀 Uber Eats Brand Store URLs Scraper

**Easily extract every Uber Eats store URL associated with any brand – McDonald's, Starbucks, Subway, you name it – at scale.** This Apify Actor turns a list of brand profile links into a ready-to-use JSON feed containing the direct URL of every restaurant in the brand's network. Perfect for local market analysis, competitive research, franchise mapping, and data enrichment.

### 🧩 Related Actors

- [Uber Eats – Brand Listing by Country](https://apify.com/datacach/ubereats-listing-brands-by-country): Automatically gathers all Uber Eats brand profile URLs for the countries you specify. Perfect if you don’t yet know the exact brand URLs to feed into this Actor.

### ✨ Key features

- Collects **100 % of live store URLs** for one or multiple Uber Eats brand pages.
- Works worldwide – automatically supports country-specific Uber Eats domains.
- Fully asynchronous Python scraper for speed and reliability.
- Clean JSON output ready for Excel, BI tools, databases, or your own API.

### 🔍 How it works

1. Provide one or more Uber Eats brand profile links as input.
2. The Actor automatically discovers every live restaurant associated with each brand, no matter where it is located.
3. All store URLs and essential metadata are saved to an Apify dataset for easy viewing, download, or API integration.

### 📝 Input parameters

| Field | Type | Description |
|-------|------|-------------|
| `brand_urls` | Array<String> **(required)** | One or more complete brand profile URLs. Example: `["https://www.ubereats.com/brand/starbucks"]` |
| `max_results` | Integer **(required)** | Maximum number of store URLs to return per brand. Default `10`. Up to `10 000`. |

Example JSON:

```json
{
  "brand_urls": [
    "https://www.ubereats.com/brand/mcdonalds"
  ],
  "max_results": 100
}
```

### 📤 Output example

```json
[
  {
    "store_url": "https://www.ubereats.com/store/mcdonalds-toronto/abc123",
    "store_slug": "/store/mcdonalds-toronto/abc123",
    "store_name": "McDonald's (Toronto)",
    "parent_url": "https://www.ubereats.com/ca/en/brand/mcdonalds-toronto-on",
    "region_code": "on",
    "city_code": "toronto-on",
    "country_code": "ca",
    "main_task_started": "2025-07-18T05:44:25Z",
    "extraction_date": "2025-07-18",
    "extraction_datetime": "2025-07-18T05:45:02Z",
    "input": {
      "brand_url": "https://www.ubereats.com/brand/mcdonalds"
    }
  }
]
```

### 💡 Popular use cases

- Franchise territory mapping & expansion planning
- Competitor density studies in food delivery platforms
- Updating store locator databases
- Market share and promotional activity tracking
- Location-based advertising campaigns

### 🚀 Quick start

#### Run in Apify Console

1. Click **Run**.
2. Paste your input JSON.
3. Watch results stream into the dataset in real time.

#### Run from code

Python:

```python
from apify_client import ApifyClient

client = ApifyClient("APIFY_API_TOKEN")
run = client.actor("username/ubereats-brands").call({
    "brand_urls": [
        "https://www.ubereats.com/brand/mcdonalds",
        "https://www.ubereats.com/brand/starbucks"
    ],
    "max_results": 1000
})
items = client.dataset(run["defaultDatasetId"]).list_items().items
print(items)
```

Node.js:

```js
import { Actor } from 'apify';

const run = await Actor.call('username/ubereats-brands', {
  brandUrls: [
    'https://www.ubereats.com/brand/mcdonalds',
    'https://www.ubereats.com/brand/subway'
  ],
  maxResults: 1000
});
const items = await Actor.dataset(run.defaultDatasetId).listItems();
console.log(items.items);
```

### 🛠️ Technical details

- Built with **Python** and the **Apify SDK**.
- Uses **httpx** for fast, concurrent HTTP requests.
- Automatic proxy rotation & retry logic emulate genuine users and bypass blocks.
- Handles internationalized URLs (accents, special characters) gracefully.
- Free Apify plan processes the first brand URL with up to 10 results; paid plans unlock full power.

### 🤝 Support

Need help or custom development? Open an issue in Apify Console – we usually respond within one business day.

***

#### 🔑 SEO keywords

Uber Eats brand scraper, Uber Eats store URLs extractor, UberEats API, restaurant data scraper, food delivery brand analysis, competitor mapping, franchise locations, web scraping tool, Apify Actor, JSON data feed.

# Actor input Schema

## `brand_urls` (type: `array`):

Enter the brand urls to search for ubereats stores (e.g., https://www.ubereats.com/brand/mcdonalds).

## `max_results` (type: `integer`):

Enter the maximum number of results to retrieve per brand url. Default is 10. If the brand url has fewer matches, fewer results will be returned.

## Actor input object example

```json
{
  "brand_urls": [
    "https://www.ubereats.com/brand/mcdonalds"
  ],
  "max_results": 10
}
```

# 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 = {
    "brand_urls": [
        "https://www.ubereats.com/brand/mcdonalds"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("datacach/ubereats-stores-discovery-by-brand-url").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 = { "brand_urls": ["https://www.ubereats.com/brand/mcdonalds"] }

# Run the Actor and wait for it to finish
run = client.actor("datacach/ubereats-stores-discovery-by-brand-url").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 '{
  "brand_urls": [
    "https://www.ubereats.com/brand/mcdonalds"
  ]
}' |
apify call datacach/ubereats-stores-discovery-by-brand-url --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,datacach/ubereats-stores-discovery-by-brand-url"
        }
    }
}
```

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/nOuBQHOHMqToGHszp/builds/9QGgerlUDSKveQ8hn/openapi.json