# UberEats Menu Scraper (`crawlerbros/ubereats-menu-scraper`) Actor Scrape full restaurant menus from UberEats. Extract restaurant info, all menu sections, items with prices, descriptions, and images. - **URL**: https://apify.com/crawlerbros/ubereats-menu-scraper.md - **Developed by:** [Crawler Bros](https://apify.com/crawlerbros) (community) - **Categories:** Other, Automation, Lead generation - **Stats:** 2 total users, 0 monthly users, 100.0% runs succeeded, 9 bookmarks - **User rating**: 5.00 out of 5 stars ## Pricing from $1.00 / 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/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use 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 ## UberEats Menu Scraper Extract complete restaurant menus from UberEats — including every section, item, price, description, and image. Get restaurant details like rating, delivery fee, address, and cuisine types in seconds. Built for market research, menu databases, price monitoring, and competitor analysis. --- ### What data does it extract? | Field | Description | |-------|-------------| | `restaurantId` | UberEats unique store ID | | `restaurantName` | Restaurant display name | | `restaurantUrl` | Direct link to the restaurant on UberEats | | `heroImageUrl` | Restaurant banner image URL | | `rating` | Average star rating | | `ratingCount` | Number of customer ratings | | `priceRange` | Price tier (`$`, `$$`, `$$$`) | | `cuisines` | Cuisine category tags (e.g. `["Burgers", "Fast Food"]`) | | `isOpen` | Whether currently open for delivery | | `address` | Street address | | `city` | City | | `deliveryFee` | Delivery fee (e.g. `Free`, `$0.49`) | | `estimatedDeliveryTime` | Estimated delivery time (e.g. `15–25 min`) | | `menu` | Full nested menu: sections → items | | `menu[].sectionName` | Section name (e.g. `Burgers`, `Drinks`, `Sides`) | | `menu[].items[].name` | Item name | | `menu[].items[].description` | Item description (when available) | | `menu[].items[].price` | Price in local currency (e.g. `5.99`) | | `menu[].items[].currency` | ISO currency code (e.g. `USD`, `GBP`) | | `menu[].items[].imageUrl` | Item photo URL | | `menu[].items[].isAvailable` | Whether currently available | | `menuSectionCount` | Total number of menu sections | | `menuItemCount` | Total number of menu items | | `itemsWithImageCount` | Number of items that have a photo | | `itemsWithDescCount` | Number of items that have a description | | `scrapedAt` | UTC timestamp of when data was collected | --- ### How to use 1. Open [UberEats](https://www.ubereats.com) and navigate to a restaurant page 2. Copy the URL from your browser's address bar (e.g. `https://www.ubereats.com/store/mcdonalds-fillmore/P21H_Lf3Se2wSmjGHfoFcQ`) 3. Paste the URL into the **Restaurant URLs** input field 4. Select the **Country** that matches the restaurant's location 5. Set **Max Items Per Restaurant** if you want to limit results 6. Click **Start** — your menu data appears in the dataset within seconds --- ### Input parameters | Parameter | Type | Required | Default | Description | |-----------|------|----------|---------|-------------| | `restaurantUrls` | List of URLs | Yes | — | UberEats restaurant page URLs to scrape | | `maxItemsPerRestaurant` | Integer | No | `200` | Maximum menu items extracted per restaurant (1–2000) | | `country` | Select | No | `us` | Country context for the delivery API (`us`, `gb`, `au`, `ca`, `in`) | --- ### Example output ```json { "restaurantId": "3f6d47fc-b7f7-49ed-b04a-68c61dfa0571", "restaurantName": "McDonald's® (Fillmore)", "restaurantUrl": "https://www.ubereats.com/store/mcdonalds-fillmore/P21H_Lf3Se2wSmjGHfoFcQ", "heroImageUrl": "https://d3i4yxtzktqr9n.cloudfront.net/...", "rating": 4.5, "ratingCount": "15000+", "priceRange": "$", "cuisines": ["American", "Burgers", "Fast Food"], "isOpen": true, "address": "1100 Fillmore St, San Francisco, CA 94115", "city": "San Francisco", "deliveryFee": "Free", "estimatedDeliveryTime": "15–25 min", "menuSectionCount": 8, "menuItemCount": 52, "itemsWithImageCount": 52, "itemsWithDescCount": 0, "menu": [ { "sectionName": "Burgers", "items": [ { "itemId": "abc-uuid", "name": "Big Mac", "price": 5.99, "currency": "USD", "imageUrl": "https://d3i4yxtzktqr9n.cloudfront.net/...", "isAvailable": true } ] } ], "scrapedAt": "2026-04-14T10:00:00Z" } ```` *** ### Use cases - **Competitive intelligence** — Monitor competitor menus and pricing across cities - **Menu database** — Build a structured database of restaurant menus at scale - **Price monitoring** — Track price changes over time with scheduled daily runs - **Market analysis** — Analyse cuisine trends and menu composition by region - **Restaurant onboarding** — Import menu data into your own ordering or review platform - **Food tech research** — Study menu diversity, item availability, and pricing patterns *** ### FAQ **Which countries are supported?**\ United States (`us`), United Kingdom (`gb`), Australia (`au`), Canada (`ca`), and India (`in`). Always select the country that matches the restaurant's physical location for best results. **Can I scrape multiple restaurants at once?**\ Yes — add as many URLs as you need to the `restaurantUrls` list. Each restaurant produces one output record. Restaurants are processed one at a time with a short polite delay between requests. **How do I find a restaurant's URL?**\ Go to [ubereats.com](https://www.ubereats.com), search for the restaurant, open its page, and copy the URL from your browser's address bar. The URL will look like: `https://www.ubereats.com/store//`. **Are item descriptions always available?**\ No — UberEats restaurants often omit item descriptions. The `itemsWithDescCount` field tells you how many items in a restaurant's menu include descriptions. Images are much more commonly available. **How fresh is the data?**\ Data is fetched live from UberEats at the time the actor runs. Use Apify's scheduling feature to run it daily for up-to-date menu and pricing data. **Does this require a proxy or login?**\ No. The actor uses UberEats' public API with no authentication, login, or proxy required. *** ### Legal This actor is for informational and research purposes only. Use in compliance with UberEats [Terms of Service](https://www.ubereats.com/legal/terms-of-use) and all applicable laws. Do not use to harm UberEats, its restaurant partners, or for unauthorized commercial data resale. # Actor input Schema ## `restaurantUrls` (type: `array`): List of UberEats restaurant page URLs to scrape. Each URL must be a valid https://www.ubereats.com/store/... URL. The store identifier is the last path segment (e.g. P21H\_Lf3Se2wSmjGHfoFcQ). ## `maxItemsPerRestaurant` (type: `integer`): Maximum number of menu items to extract per restaurant. Set lower to reduce run time for large menus. ## `country` (type: `string`): UberEats country context. Used to set the delivery location for the API request. Choose the country where the restaurants are located. ## Actor input object example ```json { "restaurantUrls": [ "https://www.ubereats.com/store/mcdonalds-fillmore/P21H_Lf3Se2wSmjGHfoFcQ" ], "maxItemsPerRestaurant": 10, "country": "us" } ``` # Actor output Schema ## `menus` (type: `string`): Dataset of scraped UberEats restaurant menus # 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 = { "restaurantUrls": [ "https://www.ubereats.com/store/mcdonalds-fillmore/P21H_Lf3Se2wSmjGHfoFcQ" ], "maxItemsPerRestaurant": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("crawlerbros/ubereats-menu-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 = { "restaurantUrls": ["https://www.ubereats.com/store/mcdonalds-fillmore/P21H_Lf3Se2wSmjGHfoFcQ"], "maxItemsPerRestaurant": 10, } # Run the Actor and wait for it to finish run = client.actor("crawlerbros/ubereats-menu-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "restaurantUrls": [ "https://www.ubereats.com/store/mcdonalds-fillmore/P21H_Lf3Se2wSmjGHfoFcQ" ], "maxItemsPerRestaurant": 10 }' | apify call crawlerbros/ubereats-menu-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=crawlerbros/ubereats-menu-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "UberEats Menu Scraper", "description": "Scrape full restaurant menus from UberEats. Extract restaurant info, all menu sections, items with prices, descriptions, and images.", "version": "1.0", "x-build-id": "FOu6lR0lItKuoZb8B" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/crawlerbros~ubereats-menu-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-crawlerbros-ubereats-menu-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/crawlerbros~ubereats-menu-scraper/runs": { "post": { "operationId": "runs-sync-crawlerbros-ubereats-menu-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/crawlerbros~ubereats-menu-scraper/run-sync": { "post": { "operationId": "run-sync-crawlerbros-ubereats-menu-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "restaurantUrls" ], "properties": { "restaurantUrls": { "title": "Restaurant URLs", "type": "array", "description": "List of UberEats restaurant page URLs to scrape. Each URL must be a valid https://www.ubereats.com/store/... URL. The store identifier is the last path segment (e.g. P21H_Lf3Se2wSmjGHfoFcQ).", "items": { "type": "string" } }, "maxItemsPerRestaurant": { "title": "Max Items Per Restaurant", "minimum": 1, "maximum": 2000, "type": "integer", "description": "Maximum number of menu items to extract per restaurant. Set lower to reduce run time for large menus.", "default": 200 }, "country": { "title": "Country", "enum": [ "us", "gb", "au", "ca" ], "type": "string", "description": "UberEats country context. Used to set the delivery location for the API request. Choose the country where the restaurants are located.", "default": "us" } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```