# Business Website Contact Extractor (`rook-data-tools/website-contact-extractor`) Actor Extract public contact details (email, phone, social profiles) that businesses publish on their own websites. Feed it URLs or another actor's dataset. Public pages only. - **URL**: https://apify.com/rook-data-tools/website-contact-extractor.md - **Developed by:** [Rook DataTools](https://apify.com/rook-data-tools) (community) - **Categories:** Lead generation, Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event 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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## Business Website Contact Extractor **Turn a list of business websites into contact details.** Give it website URLs — or the output of any lead scraper — and it visits each business's own site (homepage + contact/ about pages) and pulls the contact info the business **publishes publicly**: email addresses, phone numbers, and social profiles. ![Real run: a public chamber directory turned into a ranked call list — 15 businesses → 12 emails added → AI-scored leads with written openers](https://raw.githubusercontent.com/willowridge1234/rook-data-tools-assets/main/pipeline-proof.png) ### The gap this fills Directory scrapers (chamber, association, etc.) give you a business name and website but almost always **hide the email**. This Actor closes that gap: point it at those websites and get the emails, phones, and socials the businesses list on their own pages. It's the "enrich" step between scraping and outreach. ### Input ```json { "websites": ["https://acme.com", "https://example-cafe.com"], "inputDatasetId": "PASTE_A_SCRAPER_RUN_DATASET_ID_optional", "crawlContactPages": true, "onlyWithContact": false } ```` - **websites**: URLs to process. And/or… - **leads**: lead objects that contain a `website` field (original fields are kept + enriched). - **inputDatasetId**: enrich another Actor's output directly — paste its dataset ID. - **crawlContactPages**: also check same-site contact/about pages (default on). - **onlyWithContact**: return only sites where something was found. ### Output ```json { "website": "https://www.hannekeninsurance.com/", "primaryEmail": "info@hannekeninsurance.com", "emails": ["info@hannekeninsurance.com", "joe@hannekeninsurance.com"], "phones": ["(218) 829-3251"], "socials": ["https://www.facebook.com/hannekeninsurance/"], "contactFound": true, "pagesChecked": ["https://www.hannekeninsurance.com/", "https://www.hannekeninsurance.com/contact"], "scrapedAt": "2026-07-23T22:10:00.000Z" } ``` When you pass in leads (or a dataset), each original record is **kept and enriched** with these fields — so you can go scrape → enrich → qualify without losing any data. ### The pipeline 1. Scrape a directory (name + website) with a directory scraper. 2. **Enrich** with this Actor → add the emails/phones/socials the directory hid. 3. Qualify + write openers with the AI Lead Qualifier. ### Notes & limits - **Public pages only.** It reads what a business publishes on its own website — no logins, no CAPTCHAs, no third-party email-guessing databases. - Results are cleaned: junk and no-reply addresses are filtered out, common email obfuscation is handled, and phones are deduped and capped per record. - Sites that block bots or have no published contact simply return `contactFound: false`. *** ### 🔗 Part of a lead-generation suite (scrape → enrich → qualify) This Actor works best chained with its companions on the same account: 1. **Directory scrapers** — get businesses/members (name + website) from Wild Apricot, MembershipWorks, or ChamberMaster / GrowthZone directories. 2. **Business Website Contact Extractor** — add the emails/phones the directories hide. 3. **AI Lead Qualifier** — score each lead against your ICP and write a personalized opener. Pass a run's **dataset ID** into the next step. NOTE: cross-actor chaining needs your **apifyToken** in the input (Apify runs actors with limited permissions, so reading another run's dataset requires your token). # Actor input Schema ## `websites` (type: `array`): Business website URLs to extract contacts from (e.g. https://acme.com). Leave empty if using leads or an input dataset. ## `leads` (type: `array`): Optional: an array of lead objects that each contain a website (key: website / url / site). The original fields are kept and enriched with the found contacts. ## `inputDatasetId` (type: `string`): Enrich the output of another Actor run: paste its dataset ID (e.g. from the Wild Apricot / MembershipWorks / ChamberMaster scrapers). Each item's website is enriched with the emails those directories don't expose. Requires 'apifyToken' below (Apify runs actors with limited permissions, so reading another run's dataset needs your token). ## `apifyToken` (type: `string`): Your Apify API token — needed ONLY when using 'Input dataset ID' to read another actor run's output (chaining scrape → enrich). Get it at Console → Settings → API. Stored as a secret. Not needed if you pass websites/leads directly. ## `crawlContactPages` (type: `boolean`): In addition to the homepage, follow up to a few same-site contact/about pages for more contact details. ## `maxPagesPerSite` (type: `integer`): Homepage + up to this many pages total per site. ## `onlyWithContact` (type: `boolean`): Skip sites where no email or phone was found. ## `maxSites` (type: `integer`): Hard cap. Controls cost. ## `concurrency` (type: `integer`): How many sites to process in parallel (1-15). ## `requestDelayMs` (type: `integer`): Politeness delay. ## Actor input object example ```json { "websites": [ "https://www.hannekeninsurance.com/" ], "crawlContactPages": true, "maxPagesPerSite": 3, "onlyWithContact": false, "maxSites": 1000, "concurrency": 5, "requestDelayMs": 200 } ``` # 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 = { "websites": [ "https://www.hannekeninsurance.com/" ] }; // Run the Actor and wait for it to finish const run = await client.actor("rook-data-tools/website-contact-extractor").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 = { "websites": ["https://www.hannekeninsurance.com/"] } # Run the Actor and wait for it to finish run = client.actor("rook-data-tools/website-contact-extractor").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 '{ "websites": [ "https://www.hannekeninsurance.com/" ] }' | apify call rook-data-tools/website-contact-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=rook-data-tools/website-contact-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Business Website Contact Extractor", "description": "Extract public contact details (email, phone, social profiles) that businesses publish on their own websites. Feed it URLs or another actor's dataset. Public pages only.", "version": "0.1", "x-build-id": "uxQaY3lqgxPDdvpFD" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/rook-data-tools~website-contact-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-rook-data-tools-website-contact-extractor", "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/rook-data-tools~website-contact-extractor/runs": { "post": { "operationId": "runs-sync-rook-data-tools-website-contact-extractor", "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/rook-data-tools~website-contact-extractor/run-sync": { "post": { "operationId": "run-sync-rook-data-tools-website-contact-extractor", "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", "properties": { "websites": { "title": "Website URLs", "type": "array", "description": "Business website URLs to extract contacts from (e.g. https://acme.com). Leave empty if using leads or an input dataset.", "items": { "type": "string" } }, "leads": { "title": "Leads (with a website field)", "type": "array", "description": "Optional: an array of lead objects that each contain a website (key: website / url / site). The original fields are kept and enriched with the found contacts." }, "inputDatasetId": { "title": "Input dataset ID (optional)", "type": "string", "description": "Enrich the output of another Actor run: paste its dataset ID (e.g. from the Wild Apricot / MembershipWorks / ChamberMaster scrapers). Each item's website is enriched with the emails those directories don't expose. Requires 'apifyToken' below (Apify runs actors with limited permissions, so reading another run's dataset needs your token)." }, "apifyToken": { "title": "Apify API token (only for Input dataset ID)", "type": "string", "description": "Your Apify API token — needed ONLY when using 'Input dataset ID' to read another actor run's output (chaining scrape → enrich). Get it at Console → Settings → API. Stored as a secret. Not needed if you pass websites/leads directly." }, "crawlContactPages": { "title": "Also check contact / about pages", "type": "boolean", "description": "In addition to the homepage, follow up to a few same-site contact/about pages for more contact details.", "default": true }, "maxPagesPerSite": { "title": "Max pages per site", "minimum": 1, "maximum": 6, "type": "integer", "description": "Homepage + up to this many pages total per site.", "default": 3 }, "onlyWithContact": { "title": "Only return sites with a contact found", "type": "boolean", "description": "Skip sites where no email or phone was found.", "default": false }, "maxSites": { "title": "Max sites per run", "minimum": 1, "type": "integer", "description": "Hard cap. Controls cost.", "default": 1000 }, "concurrency": { "title": "Concurrency", "minimum": 1, "maximum": 15, "type": "integer", "description": "How many sites to process in parallel (1-15).", "default": 5 }, "requestDelayMs": { "title": "Delay between requests (ms)", "minimum": 0, "maximum": 5000, "type": "integer", "description": "Politeness delay.", "default": 200 } } }, "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 } } } } } } } } } } ```