# Restaurant Lead Finder (`george.the.developer/restaurant-lead-finder`) Actor Find restaurants in any city with verified contacts, validated emails, and lead scores. Built for restaurant tech, delivery platforms, and POS companies. - **URL**: https://apify.com/george.the.developer/restaurant-lead-finder.md - **Developed by:** [George Kioko](https://apify.com/george.the.developer) (community) - **Categories:** Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## Restaurant Lead Finder - Food & Beverage Marketing Leads Find, enrich, and score restaurant leads from Google Maps. Built for restaurant tech companies, delivery platforms, and POS companies who need verified contact data and qualified lead lists. ### What it does 1. **Scrapes Google Maps** for restaurants matching your search query 2. **Enriches each restaurant** with website data, emails, and social profiles 3. **Validates emails** to ensure deliverability 4. **Scores and ranks leads** using a restaurant-tuned scoring algorithm where reviews and social presence matter most ### Why this exists Restaurants live and die by reviews and social media. Generic lead scrapers weight phone and email equally, but in F&B, a restaurant with 500 reviews and active Instagram is a far better lead than one with just an email. This tool scores restaurants the way the industry actually works. ### Input examples ```json { "searchQuery": "restaurants in Austin TX", "maxResults": 50, "enrichWebsites": true, "validateEmails": true, "minRating": 3.5 } ```` More search queries that work well: - `"Italian restaurants San Francisco"` - `"coffee shops Portland OR"` - `"sushi restaurants New York"` - `"Mexican restaurants Los Angeles"` - `"pizza places Chicago"` - `"fine dining restaurants Miami"` ### Output fields | Field | Description | |-------|-------------| | `rank` | Position in scored results (1 = best lead) | | `leadScore` | 0-100 quality score tuned for restaurant vertical | | `restaurantName` | Restaurant name | | `vertical` | Always `"restaurant"` | | `category` | Google Maps category (cuisine type) | | `address` | Full street address | | `phone` | Phone number | | `website` | Restaurant website URL | | `rating` | Google Maps rating | | `reviewCount` | Number of Google reviews (heavily weighted) | | `emails` | Array of found emails with validation status | | `socialProfiles` | Instagram, Facebook, TikTok, etc. | | `enrichment` | Raw website enrichment data | | `aiAnalysis` | AI-powered sales intelligence (optional) | ### Scoring algorithm (restaurant-tuned) | Signal | Points | Why | |--------|--------|-----| | Has phone | +15 | Reservations and orders | | Has website | +15 | Online presence baseline | | Has email | +15 | Outreach channel | | Email validated | +10 | Deliverable = actionable | | Rating >= 4.0 | +20 | Rating is everything in F\&B | | Rating >= 4.5 | +5 bonus | Top-tier restaurant | | Reviews > 50 | +15 | Reviews are KING for restaurants | | Reviews > 200 | +10 bonus | High-volume established venue | | Social profiles | +10 | Social is critical for F\&B marketing | ### Pricing **$25 per search** (pay-per-event). Each search scrapes, enriches, validates, and scores up to your `maxResults` limit. ### Who this is for - Restaurant marketing agencies - POS system sales teams (Toast, Square, Clover) - Delivery platform partnerships (DoorDash, UberEats, Grubhub) - Restaurant tech companies (reservation systems, loyalty apps) - Food photography and branding agencies - Restaurant supply companies ### Architecture ```mermaid graph TD A[Search Query] --> B[Google Maps Scraper] B --> C[Website Enricher] C --> D[Email Validator] D --> E[Restaurant Lead Scorer] E --> F[Ranked Lead List] F --> G[Optional: AI Analysis] ``` # Actor input Schema ## `searchQuery` (type: `string`): What to search on Google Maps. Include location for best results. ## `maxResults` (type: `integer`): Maximum number of businesses to scrape from Google Maps. ## `enrichWebsites` (type: `boolean`): Enrich each business with website contact data, social profiles, and technologies. ## `validateEmails` (type: `boolean`): Validate all discovered email addresses for deliverability. ## `minRating` (type: `number`): Only include businesses with this rating or higher. Set to 0 to include all. ## Actor input object example ```json { "searchQuery": "restaurants in Austin TX", "maxResults": 20, "enrichWebsites": true, "validateEmails": true, "minRating": 0 } ``` # 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 = { "searchQuery": "restaurants in Austin TX" }; // Run the Actor and wait for it to finish const run = await client.actor("george.the.developer/restaurant-lead-finder").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 = { "searchQuery": "restaurants in Austin TX" } # Run the Actor and wait for it to finish run = client.actor("george.the.developer/restaurant-lead-finder").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 '{ "searchQuery": "restaurants in Austin TX" }' | apify call george.the.developer/restaurant-lead-finder --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=george.the.developer/restaurant-lead-finder", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Restaurant Lead Finder", "description": "Find restaurants in any city with verified contacts, validated emails, and lead scores. Built for restaurant tech, delivery platforms, and POS companies.", "version": "1.0", "x-build-id": "CZt6ryPJLuJtZvqZa" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/george.the.developer~restaurant-lead-finder/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-george.the.developer-restaurant-lead-finder", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/george.the.developer~restaurant-lead-finder/runs": { "post": { "operationId": "runs-sync-george.the.developer-restaurant-lead-finder", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/george.the.developer~restaurant-lead-finder/run-sync": { "post": { "operationId": "run-sync-george.the.developer-restaurant-lead-finder", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "searchQuery" ], "properties": { "searchQuery": { "title": "Search Query", "type": "string", "description": "What to search on Google Maps. Include location for best results." }, "maxResults": { "title": "Max Results", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum number of businesses to scrape from Google Maps.", "default": 20 }, "enrichWebsites": { "title": "Enrich Websites", "type": "boolean", "description": "Enrich each business with website contact data, social profiles, and technologies.", "default": true }, "validateEmails": { "title": "Validate Emails", "type": "boolean", "description": "Validate all discovered email addresses for deliverability.", "default": true }, "minRating": { "title": "Minimum Rating", "minimum": 0, "maximum": 5, "type": "number", "description": "Only include businesses with this rating or higher. Set to 0 to include all.", "default": 0 } } }, "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 } } } } } } } } } } ```