# Ubereats Menu Price Scraper (`chimerical_quicklime/ubereats-menu-price-scraper`) Actor

Search any chain or restaurant in any city and get every location's full Uber Eats menu with per-store prices, ratings, hours and address. No store URLs needed.

- **URL**: https://apify.com/chimerical\_quicklime/ubereats-menu-price-scraper.md
- **Developed by:** [Khrystyna Skotte](https://apify.com/chimerical_quicklime) (community)
- **Categories:** E-commerce, Lead generation, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $2.30 / 1,000 records

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?

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 & Price Scraper — by chain and city, no store URLs needed

Type a chain or restaurant name and a city. Get back every matching Uber Eats location with its
**full menu and per-store prices**, plus rating, review count, address, phone, hours and open status.

Most Uber Eats scrapers need you to paste store URLs one at a time. This one finds the stores for
you — so "Burger King in Houston" or "Chipotle in 10001" returns every location's menu in one run.

### Why per-location prices matter

Chain websites don't publish prices, because franchisees set them per store. The only place the
real price of a Whopper in a specific location is visible is a delivery platform. That makes this
the practical source for menu-price monitoring, competitor benchmarking, regional price studies
and franchise research.

### Input

| Field | Default | Notes |
|---|---|---|
| `searchQuery` | `Burger King` | Any chain or restaurant name |
| `location` | `Austin, TX` | City, ZIP, neighborhood or full address (US) |
| `maxStores` | `3` | How many matching locations to scrape (up to 100) |
| `matchChainName` | `true` | Only return stores whose name contains the search term — Uber Eats search otherwise mixes in convenience stores and similar cuisines |
| `flattenItems` | `false` | Off: one record per store with the menu nested. On: one record per menu item with store fields repeated — ideal for spreadsheets and price comparisons |
| `proxyConfiguration` | Residential, US | Required. Uber Eats refuses datacenter IPs |

### Output

One record per store (default):

```json
{
  "storeName": "Burger King (2500 E Riverside Dr)",
  "storeUrl": "https://www.ubereats.com/store/burger-king-2500-e-riverside-dr/oiBSDuqDU7q1opwegBjLmQ",
  "address": "2500 E Riverside Dr, Austin, TX 78741",
  "city": "Austin", "region": "TX", "postalCode": "78741",
  "phone": "+15124481234",
  "rating": 4.3, "reviewCount": "3,000+",
  "isOpen": true, "isOrderable": true,
  "cuisines": ["Burgers", "Fast Food", "American"],
  "currency": "USD",
  "etaText": "25–40 min",
  "hours": [{ "days": "Every Day", "hours": ["360-1439"] }],
  "itemCount": 118,
  "menu": [
    { "menuSection": "Menu", "category": "Flame-Grilled Burgers", "items": [
      { "itemName": "Whopper", "itemDescription": "Our Whopper Sandwich is a 1/4 lb of savory flame-grilled beef…",
        "price": 7.49, "priceText": "$7.49", "imageUrl": "https://tb-static.uber.com/…", "isAvailable": true, "isSoldOut": false, "hasCustomizations": true }
    ]}
  ],
  "searchQuery": "Burger King", "searchLocation": "Austin, TX", "scrapedAt": "2026-09-06T05:20:11.000Z"
}
```

With `flattenItems` on, each menu item becomes its own row carrying the store fields, so a run over
20 Burger King locations gives you a flat table of every item and price at every store.

`price` is a number in the store's currency; `priceText` is the display string. `hours` times are
minutes from midnight.

### Pricing

Charged per record: per store by default, per menu item when flattened. A typical chain location
has 80–150 items.

### Notes

- Uber Eats search returns the closest matches first. Increase `maxStores` to reach further out.
- Prices and availability are what Uber Eats shows at scrape time for delivery orders in that area.
- Menus vary by time of day (breakfast vs. all-day); the scrape reflects the current menu.

# Actor input Schema

## `searchQuery` (type: `string`):

What to search for on Uber Eats, e.g. Burger King, McDonald's, Chipotle, or any restaurant name.

## `location` (type: `string`):

Where to search. A city, ZIP code, neighborhood or full address, e.g. 'Austin, TX' or '10001'.

## `maxStores` (type: `integer`):

Maximum number of matching store locations to scrape. Each store yields its full menu with prices.

## `matchChainName` (type: `boolean`):

Uber Eats search mixes in loosely related stores (convenience stores, similar cuisines). Keep this on to return only stores whose name contains your search term.

## `flattenItems` (type: `boolean`):

Off: one record per store with the menu nested inside. On: one flat record per menu item with the store fields repeated — easiest for spreadsheets and price comparison.

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

Uber Eats refuses datacenter IPs; residential proxy is required and is the default.

## Actor input object example

```json
{
  "searchQuery": "Burger King",
  "location": "Austin, TX",
  "maxStores": 3,
  "matchChainName": true,
  "flattenItems": false,
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": [
      "RESIDENTIAL"
    ],
    "apifyProxyCountry": "US"
  }
}
```

# Actor output Schema

## `records` (type: `string`):

Dataset of stores with menus and prices (JSON).

# 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 = {};

// Run the Actor and wait for it to finish
const run = await client.actor("chimerical_quicklime/ubereats-menu-price-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 = {}

# Run the Actor and wait for it to finish
run = client.actor("chimerical_quicklime/ubereats-menu-price-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 '{}' |
apify call chimerical_quicklime/ubereats-menu-price-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,chimerical_quicklime/ubereats-menu-price-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/ccm6iaVduuj5QgLTp/builds/Gzg0BxNtk5zgbeyvj/openapi.json