# Uber Eats Scraper (`james.logantech/uber-eats-scraper`) Actor An actor designed to scrape Uber Eats APIs to help you quickly retrieve restaurant data including store names, ratings, and detailed menu items (with price and description). Use this tool to gain insights into local eateries, conduct competitive research, or power your own food discovery application - **URL**: https://apify.com/james.logantech/uber-eats-scraper.md - **Developed by:** [James](https://apify.com/james.logantech) (community) - **Categories:** Automation, Developer tools, Other - **Stats:** 55 total users, 0 monthly users, 100.0% runs succeeded, 3 bookmarks - **User rating**: No ratings yet ## Pricing $2.00 / 1,000 stores 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/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 ## Uber Eats Scraper This project is an actor designed to scrape Uber Eats APIs to help you quickly retrieve restaurant data including store names, ratings, and detailed menu items (with price and description). Use this tool to gain insights into local eateries, conduct competitive research, or power your own food discovery application. ### Features - **API Scraping:** Automatically fetches restaurant data from Uber Eats APIs. - **Data Extraction:** Returns key details including: - Store name - Ratings - Menu items (with price and description) ### Input Configuration The actor accepts the following JSON input: ~~~json { "address": "45 Victor Street, Chatswood, NSW 2067", "query": "German", "maxStores": 80 } ~~~ #### Input Fields Summary | Field | Type | Description | Default Value | |-------------|---------|------------------------------------------------------------------------------|-----------------------------------------| | `address` | String | Address to geocode (e.g., "45 Victor Street, Chatswood, NSW 2067"). | "45 Victor Street, Chatswood, NSW 2067" | | `query` | String | Search query for the Uber Eats API (e.g., "German"). | "German" | | `maxStores` | Integer | Maximum number of stores to retrieve information for. | 80 | #### Data Output The actor outputs a JSON file containing the following fields for each restaurant: | Field | Description | |-----------------|----------------------------------------------| | `store_name` | Name of the restaurant. | | `rating` | Average rating of the restaurant. | | `menu_items` | List of menu items with each item's price and description. | #### Example Output ~~~json [ { "store_name": "Wurst & Bier", "rating": 4.7, "menu_items": [ { "name": "Bratwurst", "price": "8.50", "description": "Grilled German sausage served with sauerkraut and mustard." }, { "name": "Schnitzel", "price": "15.99", "description": "Crispy breaded pork cutlet served with lemon and potato salad." } ] }, { "store_name": "Brotzeit Haus", "rating": 4.4, "menu_items": [ { "name": "Pretzel", "price": "3.99", "description": "Traditional Bavarian soft pretzel with coarse salt." }, { "name": "Obatzda", "price": "6.99", "description": "Cheesy Bavarian spread served with rustic bread." } ] } ] ~~~ # Actor input Schema ## `address` (type: `string`): Enter the address to geocode (e.g., 45 Victor Street, Chatswood, NSW 2067). ## `query` (type: `string`): Search query for the Uber Eats API (e.g., German). ## `maxStores` (type: `integer`): This is the maximum number of stores information will be retrieved for ## Actor input object example ```json { "address": "45 Victor Street, Chatswood, NSW 2067", "query": "German", "maxStores": 80 } ```` # 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 = { "address": "45 Victor Street, Chatswood, NSW 2067", "query": "German", "maxStores": 80 }; // Run the Actor and wait for it to finish const run = await client.actor("james.logantech/uber-eats-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 = { "address": "45 Victor Street, Chatswood, NSW 2067", "query": "German", "maxStores": 80, } # Run the Actor and wait for it to finish run = client.actor("james.logantech/uber-eats-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 '{ "address": "45 Victor Street, Chatswood, NSW 2067", "query": "German", "maxStores": 80 }' | apify call james.logantech/uber-eats-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=james.logantech/uber-eats-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Uber Eats Scraper", "description": "An actor designed to scrape Uber Eats APIs to help you quickly retrieve restaurant data including store names, ratings, and detailed menu items (with price and description). Use this tool to gain insights into local eateries, conduct competitive research, or power your own food discovery application", "version": "0.0", "x-build-id": "JauWHSas9MhaUVRWv" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/james.logantech~uber-eats-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-james.logantech-uber-eats-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/james.logantech~uber-eats-scraper/runs": { "post": { "operationId": "runs-sync-james.logantech-uber-eats-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/james.logantech~uber-eats-scraper/run-sync": { "post": { "operationId": "run-sync-james.logantech-uber-eats-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "address", "query" ], "properties": { "address": { "title": "Address", "type": "string", "description": "Enter the address to geocode (e.g., 45 Victor Street, Chatswood, NSW 2067)." }, "query": { "title": "Search Query", "type": "string", "description": "Search query for the Uber Eats API (e.g., German)." }, "maxStores": { "title": "Max Number of Stores Retrieved", "type": "integer", "description": "This is the maximum number of stores information will be retrieved for" } } }, "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 } } } } } } } } } } ```