# Uber Eats Email Scraper (`scraper-mind/uber-eats-email-scraper`) Actor Extract verified Uber Eats emails fast with this powerful Uber Eats Email Scraper! Use keywords, locations & custom domains to target restaurant listings. Perfect for lead gen, outreach, CRM enrichment, and more. Supports proxies & exports in CSV, JSON, Excel. - **URL**: https://apify.com/scraper-mind/uber-eats-email-scraper.md - **Developed by:** [Scraper Mind](https://apify.com/scraper-mind) (community) - **Categories:** Lead generation, Automation, Other - **Stats:** 52 total users, 0 monthly users, 100.0% runs succeeded, 2 bookmarks - **User rating**: No ratings yet ## Pricing $5.00/month + usage To use this Actor, you pay a monthly rental fee to the developer. The rent is subtracted from your prepaid usage every month after the free trial period.You also pay for the Apify platform usage, which gets cheaper the higher Apify subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#rental-actors ## 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 Email Scraper - Fast, Accurate & Affordable βš‘οΈπŸ“© Unlock the power of **Uber Eats email scraping** with the **Uber Eats Email Scraper**! Fast, Accurate & Affordable βš‘οΈπŸ‘Œ, this tool is crafted for marketers, researchers, and businesses looking to collect **Uber Eats emails** using targeted keywords. --- #### πŸ” Key Features - **Keyword-Based Search:** Enter keywords like `"john"`, `"marketing"`, and more to find specific **Uber Eats email** addresses. - **Location Filtering:** Narrow down results by targeting specific cities or regions. - **Platform Selection:** Specifically scrapes from **Uber Eats** restaurant listings. - **Custom Email Domains:** Filter emails by custom domains such as `@gmail.com`, `@yahoo.com`, and more. - **Proxy Support:** Avoid rate limits with proxy integration for seamless scraping. --- #### πŸ“š Input Parameters ```json { "keywords": ["john", "marketing"], "location": "Los Angeles", "platform": "Uber Eats", "customDomains": ["@gmail.com"], "proxyConfiguration": { "useApifyProxy": true } } ```` This **Uber Eats Email Scraper** uses the following input parameters: - **keywords:** - A list of keywords to search for (e.g., `["john", "marketing"]`). - **location:** - Specify a city or region to filter your search (optional). - **platform:** - Scrapes from **Uber Eats** only. - **customDomains:** - Target specific email domains in results (e.g., `@gmail.com`, `@business.com`). - **proxyConfiguration:** - Optional proxy settings for large-scale or stealth scraping. *** #### πŸ“ˆ Output Structure Your data will be structured like this: - **keyword:** The keyword used for the search. - **title:** The name of the restaurant or business. - **description:** Text from which the email was extracted. - **url:** The source link from Uber Eats. - **email:** The extracted email address. ##### Example Output JSON ```json [ { "keyword": "john", "title": "John's Tacos", "description": "Reach out to us at johnstacosmarketing@gmail.com", "url": "https://www.ubereats.com/store/johns-tacos/123456", "email": "johnstacosmarketing@gmail.com" } ] ``` *** #### πŸ› οΈ How to Use 1. **Enter Keywords & Domains:** - Add your target keywords and preferred email domains. 2. **Start the Actor:** - Click **Run** to begin scraping email addresses from **Uber Eats** listings. 3. **Enable Proxy (Optional):** - Add proxy configuration for higher success rate on large jobs. 4. **Export the Data:** - Export scraped **Uber Eats emails** in **JSON**, **CSV**, or **Excel** formats. *** #### πŸ‘©β€πŸŽ“ Best Use Cases - **Food Business Lead Generation:** Find emails of restaurant owners or managers. - **Marketing Campaigns:** Build mailing lists for local eateries or food service providers. - **Competitor Research:** Understand who’s operating in the Uber Eats ecosystem. - **CRM Enrichment:** Add verified **Uber Eats email** data to your contact list. *** #### πŸ’¬ Support & Feedback Need help with the **Uber Eats Email Scraper** or have a suggestion? We’re always listening. Contact our support team for quick assistance and feedback submission. # Actor input Schema ## `keywords` (type: `array`): A list of keywords to search for. ## `location` (type: `string`): Location to filter search results. ## `platform` (type: `string`): Select platform. ## `customDomains` (type: `array`): List of custom email domains ## `maxEmails` (type: `integer`): Maximum number of emails to collect. The scraper will stop once this limit is reached. Setting a higher limit allows for more potential results but doesn't guarantee reaching that number. This helps save costs by controlling scraping time. ## `engine` (type: `string`): Choose scraping engine. πŸš€ Cost Effective (New): Uses residential proxies with async requests for faster, cheaper scraping. πŸ”§ Legacy: Uses GOOGLE\_SERP proxy with traditional selectors - more reliable but slower and more expensive. ## `proxyConfiguration` (type: `object`): Configure proxies for this Actor. ## Actor input object example ```json { "keywords": [ "food" ], "location": "", "platform": "Uber Eats", "customDomains": [ "@gmail.com" ], "maxEmails": 20, "engine": "legacy" } ``` # 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 = { "keywords": [ "food" ], "location": "", "customDomains": [ "@gmail.com" ] }; // Run the Actor and wait for it to finish const run = await client.actor("scraper-mind/uber-eats-email-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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 = { "keywords": ["food"], "location": "", "customDomains": ["@gmail.com"], } # Run the Actor and wait for it to finish run = client.actor("scraper-mind/uber-eats-email-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "keywords": [ "food" ], "location": "", "customDomains": [ "@gmail.com" ] }' | apify call scraper-mind/uber-eats-email-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scraper-mind/uber-eats-email-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Uber Eats Email Scraper", "description": "Extract verified Uber Eats emails fast with this powerful Uber Eats Email Scraper! Use keywords, locations & custom domains to target restaurant listings. Perfect for lead gen, outreach, CRM enrichment, and more. Supports proxies & exports in CSV, JSON, Excel.", "version": "0.0", "x-build-id": "ebAs8v4UdITZtALCz" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scraper-mind~uber-eats-email-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scraper-mind-uber-eats-email-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scraper-mind~uber-eats-email-scraper/runs": { "post": { "operationId": "runs-sync-scraper-mind-uber-eats-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scraper-mind~uber-eats-email-scraper/run-sync": { "post": { "operationId": "run-sync-scraper-mind-uber-eats-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "keywords" ], "properties": { "keywords": { "title": "Keywords", "type": "array", "description": "A list of keywords to search for.", "default": [ "food" ], "items": { "type": "string" } }, "location": { "title": "Location", "type": "string", "description": "Location to filter search results.", "default": "" }, "platform": { "title": "Platform", "enum": [ "Uber Eats" ], "type": "string", "description": "Select platform.", "default": "Uber Eats" }, "customDomains": { "title": "Custom Email Domains", "type": "array", "description": "List of custom email domains", "default": [ "@gmail.com" ], "items": { "type": "string" } }, "maxEmails": { "title": "Max Emails", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum number of emails to collect. The scraper will stop once this limit is reached. Setting a higher limit allows for more potential results but doesn't guarantee reaching that number. This helps save costs by controlling scraping time.", "default": 20 }, "engine": { "title": "Engine", "enum": [ "cost-effective", "legacy" ], "type": "string", "description": "Choose scraping engine. πŸš€ Cost Effective (New): Uses residential proxies with async requests for faster, cheaper scraping. πŸ”§ Legacy: Uses GOOGLE_SERP proxy with traditional selectors - more reliable but slower and more expensive.", "default": "legacy" }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Configure proxies for this Actor." } } }, "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 } } } } } } } } } } ```