# Schools Email Scraper (`solid-scraper/schools-email-scraper`) Actor 🎓 Schools Email Scraper extracts verified school contact emails by country, niche, and keywords—fast and accurate. Ideal for lead gen, outreach, and marketing teams seeking targeted education prospects. 🚀 - **URL**: https://apify.com/solid-scraper/schools-email-scraper.md - **Developed by:** [SolidScraper](https://apify.com/solid-scraper) (community) - **Categories:** Lead generation, Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.99 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ### Schools Email Scraper 📬 **Schools Email Scraper** helps you find and extract contact emails for schools by combining business discovery with website email extraction. If you’re looking for a **school email scraper**, this actor automates email scraping from publicly available data, making it easier to build a **school district email list** or grow an **education email list building** workflow at scale—saving you hours of manual work. Whether you’re a marketer, researcher, or data analyst, **Schools Email Scraper** supports bulk collection by searching for schools across one or more locations, then scraping emails, phone numbers, and social links from the associated websites. --- ### Why choose Schools Email Scraper? | Feature | Benefit | | --- | --- | | ✅ **All-in-one email + contact extraction** | Extracts emails, phone numbers, and social media profiles from school websites in one run | | ✅ **Structured output for easy analysis** | Saves consistent fields to the dataset so you can map results to your CRM or spreadsheet | | ✅ **Reliability-focused scraping workflow** | Includes resilience and fallback behaviors so scraping keeps moving when some pages fail | | ✅ **Built-in proxy support for reliable scraping** | Helps improve stability during large-scale collection runs | | ✅ **Designed for bulk scale** | Uses limits (like `maxBusinesses`) to control how many school results you collect | | ✅ **Automated dataset saving** | Pushes results during execution so you don’t lose progress on interruptions | --- ### Key features - ✅ **School email discovery workflow**: Finds relevant school businesses for each location using your search term - 🌐 **Website-first contact harvesting**: Scrapes contact information from the school’s website when available - 📧 **Email extraction for outreach**: Collects `scraped_emails` per business and saves results in a structured format - 📞 **Phone and social media extraction**: Also captures `scraped_phones` and `scraped_social_media` when present - 🧾 **Detailed per-business progress fields**: Includes `pages_scraped`, `emails_found`, and `scrape_status` for transparency - 🛡️ **Resilience and fallback logic**: Includes handling for missing websites and failure states - 💾 **Real-time dataset saving**: Pushes data immediately as it’s scraped, including “no website” rows when applicable - 🔍 **Optional limits for controlled collection**: Use `maxBusinesses` and `scrapeMaxBusinessesPerLocation` to manage volume --- ### Input Provide input via an `input.json` file. Example structure: ```json { "googleMapsSearchTerm": "Schools", "googleMapsLocation": ["New York"], "maxBusinesses": 5, "scrapeMaxBusinessesPerLocation": false, "proxyConfiguration": { "useApifyProxy": true } } ```` #### Input Fields | Field | Required | Description | | --- | --- | --- | | `googleMapsSearchTerm` | Yes | The business type or niche to search for email scraping (prefilled with `Schools`). | | `googleMapsLocation` | Yes | A list of target geographic locations to search (for example: `["Miami, Florida"]` or `["New York"]`). | | `maxBusinesses` | No | The target number of businesses to find **with emails** (allowed range: 1–1000). The scraper stops when this target is reached. | | `scrapeMaxBusinessesPerLocation` | No | If enabled, collects up to `maxBusinesses` results per location. If disabled, it combines locations into one total limit. | | `proxyConfiguration` | No | Proxy settings for scraping (recommended for larger-scale runs). | | `proxyConfiguration.proxy support` | No | Enables the default Apify proxy setting for scraping. | *** ### Output The actor saves results to the **Business Contact Data** dataset in JSON format (table view is configured, and results are pushed as records during execution). Example output record shape (one dataset row): ```json { "name": "Unknown", "website": "https://example.org", "phone": "", "full_address": "123 Main St New York NY 10001 US", "city": "New York", "state": "NY", "zip": "10001", "country_code": "US", "scraped_emails": [], "scraped_phones": [], "scraped_social_media": [], "emails_found": 0, "pages_scraped": 0, "avg_rating": null, "total_reviews": null, "lat": null, "long": null, "place_id": null, "scrape_status": "no_website" } ``` > Note: When emails are found, the actor pushes a **flattened row per email** by copying the business data and adding `email_found` (the dataset transformation keeps the listed fields; phone/social/email arrays are produced by the flattening workflow). #### Output Fields | Field | Type | Description | | --- | --- | --- | | `name` | string | School/business name. | | `website` | string | Website URL discovered for the business. | | `phone` | string | Phone number associated with the discovered business (if present). | | `full_address` | string | Combined address string built from street/city/state/zip/country code. | | `city` | string | City extracted from the business location data. | | `state` | string | State extracted from the business location data. | | `zip` | string | Postal code extracted from the business location data. | | `country_code` | string | Country code extracted from the business location data. | | `scraped_emails` | array | Array of scraped email addresses found on the website (empty if none). | | `scraped_phones` | array | Array of scraped phone numbers found on the website (empty if none). | | `scraped_social_media` | array | Array of scraped social media links found on the website (empty if none). | | `emails_found` | number | Count of emails found for this business. | | `pages_scraped` | number | Number of processed URLs/steps during scraping for the business. | | `avg_rating` | number | Average rating from the discovered business data (if present). | | `total_reviews` | number | Total reviews from the discovered business data (if present). | | `lat` | number | Latitude from business location data (if present). | | `long` | number | Longitude from business location data (if present). | | `place_id` | string | Place identifier from the discovered business record (used for de-duplication). | | `scrape_status` | string | Scraping outcome status (e.g., `success`, `failed`, `error`, `no_website`). | *** ### How to use Schools Email Scraper (via Apify Console) 1. **Open Apify Console**\ Log in at https://console.apify.com and go to the **Actors** section. 2. **Find Schools Email Scraper**\ Search for **Schools Email Scraper** and open the actor details page. 3. **Configure your INPUT**\ In the **Input** panel, set: - `googleMapsSearchTerm` (defaults to `Schools`) - `googleMapsLocation` (one or more locations) 4. **Set collection limits (optional)**\ Choose `maxBusinesses` to control how many businesses with emails you want.\ If you want limits per location instead of one combined total, enable `scrapeMaxBusinessesPerLocation`. 5. **(Optional) Enable proxy support**\ Configure `proxyConfiguration` and set `proxyConfiguration.proxy support` to `true` for proxy-enabled scraping stability on larger runs. 6. **Run the actor**\ Click **Run**. Watch logs for progress updates and scraping outcomes. 7. **Review results in the dataset**\ After completion, open the actor **dataset** titled **Business Contact Data** to see your structured output fields. 8. **Export your data**\ Export from the dataset view to JSON/CSV for your workflow (CRM import, analysis, or outreach lists). No coding required—get accurate school email extraction results in minutes with **Schools Email Scraper**. ✅ *** ### Advanced features & SEO optimization - 🎯 **Engineered for “school email scraper” workflows**: Designed specifically for extracting education institution email contacts by pairing discovery with website harvesting, making it a practical **find school emails** solution. - 🔄 **Scales with clear stop conditions**: Uses your `maxBusinesses` target and can apply limits per location via `scrapeMaxBusinessesPerLocation`. - 🧠 **Built for email-oriented list building**: Works well for **school directory email scraper** and **bulk school email collection** use cases where you need many school contacts fast. - 🗃️ **Structured education data output**: Produces consistent fields for **school district email list** style datasets, including phones and social links for better enrichment. - 🛡️ **Proxy-ready scraping runs**: Includes proxy support to help maintain stability during larger scraping batches. *** ### Best use cases - 📈 **Lead generation for school programs**: Quickly assemble a **school contact email extractor** dataset for outreach campaigns. - 🎓 **College and university pipeline research**: Build a **college email scraper** workflow for education email list building across regions. - 🏫 **School district directory enrichment**: Create a **school district email list** enriched with phone numbers and social media links. - 🧪 **Education marketing analytics**: Analyze email availability (`emails_found`, `pages_scraped`) alongside business rating/reviews for quality scoring. - 💼 **Recruitment and communications teams**: Use extracted contacts to streamline internal marketing and communications planning. - 🧰 **Data analysts & researchers**: Combine geo, address, and email extraction results for structured dataset analysis. - ✉️ **Bulk outreach operations**: Use as a **bulk school email collection** tool to move faster from discovery to campaign-ready lists. *** ### Technical specifications - **Supported Input Formats** - ✅ `googleMapsSearchTerm`: string (business niche; default prefill: `Schools`) - ✅ `googleMapsLocation`: array of strings (locations) - **Proxy Support** - ✅ `proxyConfiguration` (with `proxyConfiguration.proxy support` supported) - Recommended for larger scraping runs - **Retry Mechanism** - ✅ Includes retries and fallbacks for resilience (high-level behavior) - Exact retry counts/delays are controlled internally by the scraping components - **Dataset Structure** - ✅ Dataset: **Business Contact Data** - ✅ Dataset rows include fields like `name`, `website`, `scraped_emails`, `emails_found`, `scrape_status`, and more - **Rate Limits & Performance** - ⚙️ Designed for batch scraping with concurrency and controlled limits via `maxBusinesses` - **Limitations** - ⚠️ If a business has **no website** available, scraping cannot extract emails from the site and the row is pushed with a `scrape_status` such as `no_website`. - ⚠️ Results depend on the availability of publicly accessible contact details. *** ### FAQ #### How do I find school emails with Schools Email Scraper? Schools Email Scraper first discovers relevant school businesses using your `googleMapsSearchTerm` and `googleMapsLocation`, then scrapes the business website to extract emails and related contact details. This makes it practical for “find school emails” and “school email scraper” workflows. #### What does the actor save to the dataset? It saves fields configured under **Business Contact Data**, including business identity and contact details like `website`, `scraped_emails`, `scraped_phones`, `scraped_social_media`, plus metrics such as `emails_found`, `pages_scraped`, and `scrape_status`. #### Can I control how many schools are collected? Yes. Use `maxBusinesses` to set the target number of businesses with emails. If you enable `scrapeMaxBusinessesPerLocation`, the actor will aim for up to `maxBusinesses` results per location; otherwise it uses a single combined total limit. #### Do I need a proxy? You don’t have to, but proxy configuration is available via `proxyConfiguration`. Proxy support is recommended for larger-scale scraping runs to help improve stability. #### Does it extract phone numbers and social media too? Yes. Along with `scraped_emails`, the actor also attempts to extract `scraped_phones` and `scraped_social_media` from the school website when available. #### Will it save results when no website is found? Yes. For businesses without a website, it pushes a dataset row with empty `scraped_emails` / `scraped_phones` / `scraped_social_media`, `emails_found` set to `0`, `pages_scraped` set to `0`, and a `scrape_status` such as `no_website` (unless email-only behavior prevents saving, depending on the run mode). #### Is this suitable for bulk school email collection? ✅ Yes. The actor is built for batch runs with dataset saving during execution, and it’s designed for workflows like **school directory email scraper** and **education email list building**. *** ### Support & feature requests Have feedback on **Schools Email Scraper** or want improvements for your **school email scraper** workflow? 💡 - 💡 **Feature Requests**: For example, you can request CSV export options, custom email handling rules, or tighter enrichment output for education institution email scraper use cases. - 📧 **Contact**: Send us a message at Your input helps shape the roadmap for future versions of **Schools Email Scraper**. 🚀 *** *If you’re after the most comprehensive, SEO-friendly “schools email scraper” workflow for education lead generation, start a run with Schools Email Scraper today—engineered to help you collect school contact emails at scale.* *** ### Disclaimer **This tool accesses publicly accessible sources only.** It does not access private profiles, authenticated content, password-protected pages, or other restricted data. You are responsible for complying with applicable laws (including GDPR/CCPA where relevant), spam regulations, and each website’s terms of service. For data-removal requests, contact . Please use **Schools Email Scraper** responsibly, ethically, and only for legitimate purposes. ✅ # Actor input Schema ## `googleMapsSearchTerm` (type: `string`): Enter the business type or niche for email scraper (e.g., 'coffee shops', 'dentists'). ## `googleMapsLocation` (type: `array`): Target geographic location for the email scraper (e.g., 'Miami, Florida'). ## `maxBusinesses` (type: `integer`): Target number of businesses to find (1-1000). The scraper will stop when this target is reached. ## `scrapeMaxBusinessesPerLocation` (type: `boolean`): If enabled, the scraper will collect up to `maxBusinesses` results per location. If disabled, it combines all locations up to a single total limit. ## `proxyConfiguration` (type: `object`): Proxy settings for scraping. Recommended for large-scale scraping. ## Actor input object example ```json { "googleMapsSearchTerm": "Schools", "googleMapsLocation": [ "New York" ], "maxBusinesses": 5, "scrapeMaxBusinessesPerLocation": false, "proxyConfiguration": { "useApifyProxy": true } } ``` # 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 = { "googleMapsSearchTerm": "Schools", "googleMapsLocation": [ "New York" ], "maxBusinesses": 5, "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("solid-scraper/schools-email-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 = { "googleMapsSearchTerm": "Schools", "googleMapsLocation": ["New York"], "maxBusinesses": 5, "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("solid-scraper/schools-email-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 '{ "googleMapsSearchTerm": "Schools", "googleMapsLocation": [ "New York" ], "maxBusinesses": 5, "proxyConfiguration": { "useApifyProxy": true } }' | apify call solid-scraper/schools-email-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=solid-scraper/schools-email-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Schools Email Scraper", "description": "🎓 Schools Email Scraper extracts verified school contact emails by country, niche, and keywords—fast and accurate. Ideal for lead gen, outreach, and marketing teams seeking targeted education prospects. 🚀", "version": "1.0", "x-build-id": "2MtlnMyOr6OsXv1uk" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/solid-scraper~schools-email-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-solid-scraper-schools-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/solid-scraper~schools-email-scraper/runs": { "post": { "operationId": "runs-sync-solid-scraper-schools-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/solid-scraper~schools-email-scraper/run-sync": { "post": { "operationId": "run-sync-solid-scraper-schools-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "googleMapsLocation", "googleMapsSearchTerm" ], "properties": { "googleMapsSearchTerm": { "title": "Search Term", "type": "string", "description": "Enter the business type or niche for email scraper (e.g., 'coffee shops', 'dentists').", "default": "Schools" }, "googleMapsLocation": { "title": "Location", "type": "array", "description": "Target geographic location for the email scraper (e.g., 'Miami, Florida').", "default": [ "New York" ], "items": { "type": "string" } }, "maxBusinesses": { "title": "Maximum Businesses With Emails", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Target number of businesses to find (1-1000). The scraper will stop when this target is reached.", "default": 5 }, "scrapeMaxBusinessesPerLocation": { "title": "Scrape Max Businesses Per Location", "type": "boolean", "description": "If enabled, the scraper will collect up to `maxBusinesses` results per location. If disabled, it combines all locations up to a single total limit.", "default": false }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Proxy settings for scraping. Recommended for large-scale scraping." } } }, "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 } } } } } } } } } } ```