Uber Eats Email Scraper

Under maintenance

Pricing

$5.00/month + usage

Try for free

Go to Apify Store

Uber Eats Email Scraper

Under maintenance

Try for free

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.

Pricing

$5.00/month + usage

Rating

0.0

(0)

Developer

Scraper Mind

Maintained by Community

Actor stats

Bookmarked

Total users

Monthly active users

0.19 hours

Issues response

2 months ago

Last modified

Categories

Lead generation

Automation

Other

You can access the Uber Eats Email Scraper programmatically from your own applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in Integrations settings in Apify Console.

Python

JavaScript

CLI

OpenAPI

HTTP

MCP

{
  "openapi": "3.0.1",
  "info": {
    "version": "0.0",
    "x-build-id": "QMuHp6voBwwRhtSxP"
  },
  "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": [
              "founder",
              "marketing"
            ],
            "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. 🚀 Cost Effective (New): Uses residential proxies with async requests for faster, cheaper scraping. 🔧 Legacy: Uses GOOGLE_SERP proxy with traditional selectors - more reliable but slower and more expensive.",
            "default": "cost-effective"
          },
          "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
                  }
                }
              }
            }
          }
        }
      }
    }
  }
}

Uber Eats Email Scraper OpenAPI definition

OpenAPI is a standard for designing and describing RESTful APIs, allowing developers to define API structure, endpoints, and data formats in a machine-readable way. It simplifies API development, integration, and documentation.

OpenAPI is effective when used with AI agents and GPTs by standardizing how these systems interact with various APIs, for reliable integrations and efficient communication.

By defining machine-readable API specifications, OpenAPI allows AI models like GPTs to understand and use varied data sources, improving accuracy. This accelerates development, reduces errors, and provides context-aware responses, making OpenAPI a core component for AI applications.

You can download the OpenAPI definitions for Uber Eats Email Scraper from the options below:

OpenAPI.json

If you’d like to learn more about how OpenAPI powers GPTs, read our blog post.

You can also check out our other API clients:

Uber Eats Email Scraper API in Python

Uber Eats Email Scraper API in JavaScript

Uber Eats Email Scraper API through CLI

Uber Eats Email Scraper API

Uber Eats Scraper

natanielsantos/uber-eats-scraper

This no-code tool allows you to scrape data from Uber Eats restaurants, like name, categories, location, menu items, and more.

Nataniel Santos

163

Uber Eats Store Search Scraper 🍽️

easyapi/uber-eats-store-search-scraper

Extract detailed restaurant data from Uber Eats search results. Get comprehensive information about stores including ratings, status, location, and images. Perfect for market research, competitor analysis, and food delivery insights. 🍽️🔍

EasyApi

🍴Uber Eats Scraper [RENTAL]

borderline/ubereats-scraper

Rental - Fast and cost-effective Uber Eats Scraper requiring just a search query and location. Instantly retrieve comprehensive restaurant data, including names, ratings, locations, reviews and menus. Seamlessly integrates with tools like Google Sheets, and more, offering a highly efficient solution

borderline

211

4.8

🍴Uber Eats Scraper [PPR]

borderline/uber-eats-scraper-ppr

Pay per result - Fast and cost-effective Uber Eats Scraper requiring just a search query and location. Instantly retrieve comprehensive restaurant data, including names, phone, ratings, locations, and menus. Seamlessly integrates, offering a highly efficient data solution.

borderline

162

5.0

Ubereats Stores Search By Location And Keyword

datacach/ubereats-stores-search-by-location-and-keyword

Experience instant restaurant insights with Uber Eats Discovery by Location and Keyword Scraper. Enter a search and location to get real-time data—no URLs needed. Perfect for quick market research and competitor analysis, all in one efficient, easy-to-use tool.

DataCach

Uber Eats Scraper | With Menu & Numbers | $3 / 1k

fatihtahta/uber-eats-menu-scraper

Scrape restaurants and groceries from Uber Eats including phone numbers, menu, location, cuisines, and more. export-ready for price and menu tracking, market research, competitor analysis, and lead generation. $3 per 1000 listings.

Fatih Tahta

5.0

UberEats Menu Scraper – Full Product, Price & Modifier

crscrapers/ubereats-menu-scraper---v2

Extract full UberEats menus with one URL. Get restaurant name, images, categories, products, prices, descriptions, modifiers, and customization options. Perfect for restaurant owners tracking competitors and optimizing their own pricing and menus.

CMQ

Deliveroo Scraper

fantomx/deliveroo-scraper

🦘Scrape your favorite restaurant from Deliveroo simply by enter a location URL

fantomX

Restaurant Menu Scraper

learned_district/wedo-scrape-menu

Menu Scraper: AI-powered Apify Actor for discovering and extracting restaurant menus. Scrape detailed menu items, descriptions, and prices from official websites. Ideal for web scraping, food data aggregation, and restaurant analysis.

Benjamin

🍽️ Grubhub Restaurant Reviews Scraper

easyapi/grubhub-restaurant-reviews-scraper

Efficiently extract comprehensive customer reviews, ratings, and order details from Grubhub restaurant pages. Get valuable insights into customer satisfaction, menu item popularity, and sentiment analysis with this powerful and reliable scraper.

EasyApi

5.0