# Uber Eats Restaurants & Menus Scraper (`neuro-scraper/uber-eats-restaurants-menus-scraper`) Actor Uber Eats Scraper SD - Uber Eats Scraper is a restaurant data tool that extracts public Uber Eats stores and full menus from store links or city pages: name, address, phone, cuisines, rating, reviews, opening hours, menu sections, items and prices. - **URL**: https://apify.com/neuro-scraper/uber-eats-restaurants-menus-scraper.md - **Developed by:** [Neuro Scraper](https://apify.com/neuro-scraper) (community) - **Categories:** E-commerce, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.49 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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? 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 Scraper - restaurants and full menus from Uber Eats The Uber Eats Scraper extracts public Uber Eats stores with their complete menus: store name, cuisines, price range, rating and review count, phone, full address with coordinates, opening hours, images, and every menu section with item names, descriptions and prices. Give city slugs or paste store, city, category or neighborhood links. No Uber Eats account, login or delivery address is used. #### Input | Field | Type | Default | Meaning | |---|---|---|---| | `cities` | list | `austin-tx` (prefill) | City part of an Uber Eats city URL: `austin-tx`, `gb/london-eng`, `fr/paris-idf`; "Austin, TX" also works for US cities | | `startUrls` | list | empty | Store URLs and/or city, category (`/category/austin-tx/pizza`) or neighborhood pages | | `maxItems` | integer | `20` | Total stores to return | | `includeMenu` | boolean | `true` | Put the full menu in each row | | `maxMenuItems` | integer | `0` | Cap menu items per store (0 = all) | | `proxyConfiguration` | proxy | Apify RESIDENTIAL | Proxy used for Uber Eats | #### Output (one row per store) | Field | Meaning | |---|---| | `storeId`, `name`, `url` | Uber Eats store id, name, link | | `cuisines`, `priceRange`, `rating`, `reviewCount` | Cuisine tags, $ to $$$$, average rating, number of ratings | | `phone`, `street`, `city`, `region`, `postalCode`, `country`, `latitude`, `longitude` | Contact and location | | `openingHours` | List of `{days, opens, closes}` | | `imageUrl`, `images` | Store images | | `menuSectionCount`, `menuItemCount`, `currency`, `minItemPrice`, `maxItemPrice` | Menu summary | | `menu` | Sections with items `{name, description, price}` (omitted when `includeMenu` is off) | | `source`, `scrapedAt` | Input that produced the row, time | #### Measured limits (2026-10-03, local runs) - About 2 s per store (one 1-2 MB page each) plus one request per list page of 20-26 stores: 20 stores in 39 s, 45 in 97 s. - City pages kept giving new stores up to page 20 (the deepest page tested). - Grocery, convenience and wholesale stores (Costco, Co-op, Sainsbury's) publish no menu in their page: their rows have store data and `menuItemCount` 0. - Delivery fees, ETAs and live open/closed state depend on a delivery address and are not included. - Tested without proxy from one residential IP; no bot challenge was met. A challenge is never solved: the Actor retries on a new IP, then skips the target with an error in the log. #### Example ```json {"cities": ["austin-tx", "gb/london-eng"], "maxItems": 100, "maxMenuItems": 50} ``` # Actor input Schema ## `cities` (type: `array`): One per line: the city part of an Uber Eats city URL - austin-tx (from ubereats.com/city/austin-tx) or, outside the US, with the country: gb/london-eng, fr/paris-idf. 'Austin, TX' also works for US cities. ## `startUrls` (type: `array`): Store URLs (https://www.ubereats.com/store//) and/or city, category or neighborhood pages (https://www.ubereats.com/category/austin-tx/pizza). ## `maxItems` (type: `integer`): Stops after this many stores in total (one request per store, plus one per list page of ~20 stores). ## `includeMenu` (type: `boolean`): Adds every menu section with its items, descriptions and prices to the store row. Counts and min/max item price are always included. ## `maxMenuItems` (type: `integer`): 0 = the whole menu. A smaller number keeps rows light. ## `proxyConfiguration` (type: `object`): Residential proxies (the default) are recommended. ## Actor input object example ```json { "cities": [ "austin-tx" ], "startUrls": [], "maxItems": 20, "includeMenu": true, "maxMenuItems": 0, "proxyConfiguration": { "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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "cities": [ "austin-tx" ], "startUrls": [], "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }; // Run the Actor and wait for it to finish const run = await client.actor("neuro-scraper/uber-eats-restaurants-menus-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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "cities": ["austin-tx"], "startUrls": [], "proxyConfiguration": { "useApifyProxy": True, "apifyProxyGroups": ["RESIDENTIAL"], }, } # Run the Actor and wait for it to finish run = client.actor("neuro-scraper/uber-eats-restaurants-menus-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 '{ "cities": [ "austin-tx" ], "startUrls": [], "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }' | apify call neuro-scraper/uber-eats-restaurants-menus-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "type": "http", "url": "https://mcp.apify.com/?tools=fetch-actor-details,neuro-scraper/uber-eats-restaurants-menus-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 ` 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/i5eMGgRYqNgwF0Zpf/builds/l5dHiTSYkA0bCi1F4/openapi.json