# Email Validation & Disposable Email Detector (`mayno/email-risk-scorer`) Actor Detect disposable emails, score email risk, and protect your signup flow. Block fake signups from Mailinator, Guerrilla Mail, and 500+ temporary email providers. - **URL**: https://apify.com/mayno/email-risk-scorer.md - **Developed by:** [Amine Feriani](https://apify.com/mayno) (community) - **Categories:** Developer tools - **Stats:** 1 total users, 1 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $1.00 / 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 ## Email Risk Scorer & Disposable Email Detector **Protect your signup flow from fake accounts, disposable emails, and fraudulent registrations.** This actor analyzes email addresses and returns a comprehensive risk assessment, helping you block temporary emails, identify free providers, validate MX records, and detect role-based addresses — all in real-time. --- ### 🎯 What It Does Every SaaS product, e-commerce platform, or community site faces the same problem: **fake signups from disposable emails**. Someone creates a throwaway account with `user123@guerrillamail.com`, abuses your free trial, and disappears. This actor solves that by analyzing each email and returning: - ✅ **Disposable email detection** — Blocks 500+ temporary email providers (Mailinator, Guerrilla Mail, 10MinuteMail, etc.) - ✅ **Free provider detection** — Identifies Gmail, Yahoo, Outlook, and 100+ other free email services - ✅ **Business email validation** — Confirms legitimate company domains with valid MX records - ✅ **MX record verification** — Checks if the domain can actually receive emails - ✅ **Email provider identification** — Detects Google Workspace, Microsoft 365, Zoho, and more - ✅ **Role-based email detection** — Flags generic addresses like `info@`, `admin@`, `noreply@` - ✅ **Risk level scoring** — Returns `low`, `medium`, or `high` risk classification --- ### 🚀 Use Cases #### SaaS & Freemium Products Block fake signups during free trial registration. Once integrated into your signup flow, this actor runs on every new user registration — protecting your product from abuse. #### E-Commerce & Marketplaces Detect throwaway emails at checkout to reduce fraud, chargebacks, and fake orders. #### Email Marketing & Lead Generation Clean your email lists before sending campaigns. Remove disposable emails to protect your sender reputation and improve deliverability. #### Community Platforms & Forums Prevent spam accounts from flooding your platform with fake registrations. #### CRM & Lead Scoring Score leads based on email quality. A lead with a disposable email address is worthless — filter them out before they enter your pipeline. --- ### 📋 Input The actor accepts a simple JSON input with a list of email addresses: ```json { "emails": [ "test@mailinator.com", "ceo@stripe.com", "user@gmail.com", "admin@company.com" ] } ```` **Input Parameters:** | Parameter | Type | Required | Description | |-----------|------|----------|-------------| | `emails` | array | Yes | List of email addresses to analyze (max 10,000 per run) | *** ### 📤 Output For each email, you get a comprehensive risk assessment: ```json { "email": "test@mailinator.com", "isValid": true, "isDisposable": true, "isFreeProvider": false, "isBusinessEmail": false, "isRoleBasedEmail": false, "domain": "mailinator.com", "hasMXRecord": true, "mxProvider": "mail.mailinator.com", "riskLevel": "high", "reasons": [ "Disposable/temporary email provider" ] } ``` **Output Fields:** | Field | Type | Description | |-------|------|-------------| | `email` | string | The analyzed email address (normalized to lowercase) | | `isValid` | boolean | Whether the email has valid syntax | | `isDisposable` | boolean | Whether the domain is a known disposable/temporary email provider | | `isFreeProvider` | boolean | Whether the domain is a free email service (Gmail, Yahoo, etc.) | | `isBusinessEmail` | boolean | Whether it's a legitimate business email (valid MX + not disposable/free) | | `isRoleBasedEmail` | boolean | Whether it uses a generic prefix like `info@`, `admin@`, `support@` | | `domain` | string | The email domain (e.g., `mailinator.com`) | | `hasMXRecord` | boolean | Whether the domain has valid MX (mail exchange) records | | `mxProvider` | string | The email provider identified from MX records (e.g., "Google Workspace") | | `riskLevel` | string | Overall risk assessment: `low`, `medium`, or `high` | | `reasons` | array | List of reasons explaining the risk classification | *** ### 🎓 How Risk Levels Work The actor evaluates multiple signals and assigns one of three risk levels: #### 🟢 **Low Risk** (Safe to accept) - Valid business email with MX records - Examples: `ceo@stripe.com`, `hello@notion.so` - Free providers like Gmail/Yahoo with valid syntax #### 🟡 **Medium Risk** (Review manually) - Missing MX records - Role-based emails on otherwise valid domains - Example: `team@startup-without-mx-setup.com` #### 🔴 **High Risk** (Block or flag) - Disposable/temporary email providers - Invalid email syntax - No MX records + other red flags - Examples: `user@mailinator.com`, `fake@guerrillamail.com` # Actor input Schema ## `emails` (type: `array`): List of email addresses to analyze for risk. Maximum 10,000 emails per run. ## Actor input object example ```json { "emails": [ "test@mailinator.com", "ceo@stripe.com", "user@gmail.com" ] } ``` # Actor output Schema ## `results` (type: `string`): Complete email risk analysis for all processed emails # 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 = { "emails": [ "test@mailinator.com", "ceo@stripe.com", "user@gmail.com" ] }; // Run the Actor and wait for it to finish const run = await client.actor("mayno/email-risk-scorer").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 = { "emails": [ "test@mailinator.com", "ceo@stripe.com", "user@gmail.com", ] } # Run the Actor and wait for it to finish run = client.actor("mayno/email-risk-scorer").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 '{ "emails": [ "test@mailinator.com", "ceo@stripe.com", "user@gmail.com" ] }' | apify call mayno/email-risk-scorer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=mayno/email-risk-scorer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Email Validation & Disposable Email Detector", "description": "Detect disposable emails, score email risk, and protect your signup flow. Block fake signups from Mailinator, Guerrilla Mail, and 500+ temporary email providers.", "version": "0.1", "x-build-id": "AxwUasaPOZGRQn3Uc" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/mayno~email-risk-scorer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-mayno-email-risk-scorer", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/mayno~email-risk-scorer/runs": { "post": { "operationId": "runs-sync-mayno-email-risk-scorer", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/mayno~email-risk-scorer/run-sync": { "post": { "operationId": "run-sync-mayno-email-risk-scorer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "emails" ], "properties": { "emails": { "title": "Email Addresses", "minItems": 1, "maxItems": 10000, "uniqueItems": true, "type": "array", "description": "List of email addresses to analyze for risk. Maximum 10,000 emails per run.", "items": { "type": "string" } } } }, "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 } } } } } } } } } } ```