# Uber Eats Scraper — Menus, Item Prices & Ratings | API (`bovi/ubereats-menu`) Actor

Scrape any **Uber Eats** restaurant menu into FLAT rows — one row per item with **price at the top level**, plus store rating, cuisine, address & phone on every row. No nested digging, no browser. Reliable residential-proxy access. Priced per menu — well below per-menu incumbents.

- **URL**: https://apify.com/bovi/ubereats-menu.md
- **Developed by:** [Vitalii Bondarev](https://apify.com/bovi) (community)
- **Categories:** E-commerce, Developer tools
- **Stats:** 3 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $96.03 / 1,000 menu scrapeds

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

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

## 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 Menu Scraper 🍔

Turn any **Uber Eats** restaurant page into a clean, **flat** menu table — one
row per item with the **price right at the top level**. No nested JSON to dig
through, no browser, no manual copy-paste.

### What you get (one row per menu item)

| field | description |
|---|---|
| `restaurant_name` | Store / restaurant name |
| `section` | Menu section breadcrumb (e.g. `Breakfast > Featured items`) |
| `item_name` | Menu item name |
| `price_usd` | **Item price, top-level number** (no nested `offers.price`) |
| `price_currency` | Currency code (e.g. `USD`) |
| `description` | Item description |
| `image_url` | Item photo |
| `rating` / `num_ratings` | Store rating + review count (repeated on every row) |
| `cuisine` | Cuisine tags |
| `price_range` | Store price range (e.g. `$$`) |
| `phone`, `address`, `city`, `state`, `zip`, `country` | Store location & contact |
| `store_url`, `source_url` | Links |
| `parse_confidence`, `warnings` | Structural-drift signal — you never pay for a broken parse |
| `scraped_at` | UTC timestamp |

### How to use

1. Open a restaurant on [ubereats.com](https://www.ubereats.com) and copy the
   page URL (`https://www.ubereats.com/store/{name}/{id}`).
2. Paste one or more URLs into **Store URLs**.
3. Run. You get a flat table of every menu item with its price.

### Input

- **Store URLs** — list of Uber Eats `/store/` URLs.
- **Max Menus** — cap on number of menus (0 = unlimited).
- **Delay between requests** — politeness delay (default 1.5s).
- **Proxy** — Apify **RESIDENTIAL** by default (recommended for reliable access).

### Pricing & reliability

- Billed **per menu** (one charge per restaurant successfully scraped), no
  matter how many items the menu has — far cheaper than tools that meter the
  same per-menu unit at a premium.
- **You never pay for a failed parse**: if the menu structure drifts, rows are
  returned with a low `parse_confidence` and the menu is **not charged**.
- Reliable access via residential proxy; runs entirely on the Apify platform.

### More scrapers from our toolkit

Building a data pipeline? These actors pair well with this one — each runs on your own Apify account with the same pay-per-result pricing, no subscription:

- [Vinted Marketplace](https://apify.com/bovi/vinted-marketplace)
- [Allegro Offers](https://apify.com/bovi/allegro-offers)
- [Amazon Products Scraper](https://apify.com/bovi/amazon-products-scraper)
- [Autoscout24 Cars Scraper](https://apify.com/bovi/autoscout24-cars-scraper)
- [Competitor Price Monitor](https://apify.com/bovi/competitor-price-monitor)
- [Craigslist Scraper](https://apify.com/bovi/craigslist-scraper)

Chain any of them together from the **Integrations** tab (the *Run succeeded* trigger) to build a multi-step workflow — one actor's output feeds the next.

# Actor input Schema

## `storeUrls` (type: `array`):

List of Uber Eats restaurant/store URLs to scrape. Each must be in the format https://www.ubereats.com/store/{slug}/{storeId}. Open a restaurant on ubereats.com and copy the URL. Example: \['https://www.ubereats.com/store/zuckers-bagels-fidis/YK9UYIbxTd2Gappp2N4pgQ']. Returns one flat row per menu item with the price at the top level.

## `maxMenus` (type: `integer`):

Hard cap on how many store menus to scrape across all URLs. 0 = unlimited. Each successfully scraped menu is exactly one charge, regardless of how many items it contains.

## `requestDelay` (type: `number`):

Seconds to wait between fetching consecutive store URLs. Default 1.5s is polite and reduces rate-limiting. Increase for large batches.

## `proxyConfiguration` (type: `object`):

Proxy settings. Default: Apify RESIDENTIAL proxy (strongly recommended — datacenter IPs are frequently rate-limited by Uber Eats). Buyers pay for proxy usage via Apify platform compute.

## Actor input object example

```json
{
  "storeUrls": [
    "https://www.ubereats.com/store/zuckers-bagels-fidis/YK9UYIbxTd2Gappp2N4pgQ"
  ],
  "maxMenus": 0,
  "requestDelay": 1.5,
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": [
      "RESIDENTIAL"
    ]
  }
}
```

# Actor output Schema

## `results` (type: `string`):

Dataset of menu-item rows (restaurant\_name, section, item\_name, price\_usd, description, rating, cuisine, city, source\_url, scraped\_at, parse\_confidence).

# 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 = {
    "storeUrls": [
        "https://www.ubereats.com/store/zuckers-bagels-fidis/YK9UYIbxTd2Gappp2N4pgQ"
    ],
    "maxMenus": 0
};

// Run the Actor and wait for it to finish
const run = await client.actor("bovi/ubereats-menu").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 = {
    "storeUrls": ["https://www.ubereats.com/store/zuckers-bagels-fidis/YK9UYIbxTd2Gappp2N4pgQ"],
    "maxMenus": 0,
}

# Run the Actor and wait for it to finish
run = client.actor("bovi/ubereats-menu").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 '{
  "storeUrls": [
    "https://www.ubereats.com/store/zuckers-bagels-fidis/YK9UYIbxTd2Gappp2N4pgQ"
  ],
  "maxMenus": 0
}' |
apify call bovi/ubereats-menu --silent --output-dataset

```

## MCP server setup

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

```

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/XizjIBR0w7ZHlEOvP/builds/51c5TvBPzAk5WWyzP/openapi.json