# Ada Dentists Email Scraper (`scraperoka/ada-dentists-email-scraper`) Actor 📧 Ada Dentists Email Scraper extracts verified dentist emails from Ada Dentists listings. Fast, accurate, and GDPR-friendly\*—perfect for B2B outreach, lead generation, and marketing campaigns. 🚀📈 Get targeted dental contacts in minutes! - **URL**: https://apify.com/scraperoka/ada-dentists-email-scraper.md - **Developed by:** [Scraperoka](https://apify.com/scraperoka) (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 $0.01 / 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 ### Ada Dentists Email Scraper 📬 **Ada Dentists Email Scraper** is a purpose-built tool that searches for dental businesses in your chosen location(s) and extracts contact information—especially email addresses—so you can build targeted outreach lists faster. Whether you’re a marketer, recruiter, data analyst, or local researcher, Ada dentists email scraper workflows help you turn publicly available business listings into structured lead data. It’s ideal for creating an **Ada OK dentist email list**, sourcing **local dentist email leads**, and powering **dentist lead generation email** campaigns at scale—saving you hours of manual work. --- ### 🚀 Why choose Ada Dentists Email Scraper? | Feature | Benefit | |---|---| | ✅ **All-in-one business + website contact extraction** | Finds businesses, then extracts emails, phone numbers, and social media profiles from their websites in one workflow | | ✅ **Email-only mode support (filtering)** | Lets you save only businesses that actually have emails, which improves list quality for targeted outreach | | ✅ **Reliability-focused scraping flow** | Includes resilience with fallbacks so you can keep running without babysitting failures | | ✅ **Structured, dataset-ready output** | Produces consistent fields (e.g., `name`, `website`, `scraped_emails`, `email_found`, `scrape_status`) for easy CRM import | | ✅ **Built for scale with practical limits** | Uses `maxBusinesses` and location controls so you can manage batch sizes for **B2B dentist email database** building | | ✅ **Proxy configuration support** | Helps you run larger batches with **built-in proxy support for reliable scraping** | --- ### 🔑 Key features - 🧠 **Dentist contact extraction from websites**: Scrapes business websites and extracts `scraped_emails`, `scraped_phones`, and `scraped_social_media` for enriched **Ada dental clinic email outreach** - 🎯 **Location-targeted results**: Uses your `googleMapsLocation` list to focus on the areas you care about (great for “dental practice email scraper Ada” use cases) - 📦 **Flattened email rows for outreach**: Pushes one dataset row per `email_found`, making it simple to turn into **dentist email scraping tool** pipelines - 💾 **Real-time dataset saving**: Each business’s results are pushed immediately as they’re found (so runs are resilient to interruptions) - 🛡️ **Proxy configuration option**: Includes a `proxyConfiguration` input so you can use proxies when scraping at volume - 🔄 **Operational resilience**: Applies stopping conditions and city-bound filtering to keep results relevant to your target city - 📊 **Quality fields included**: Outputs `emails_found`, `pages_scraped`, and `avg_rating` so you can prioritize leads that look more established - ✅ **Email-only output control**: If enabled (via `email_only_results` in the pipeline), you only save rows that actually have emails (useful for **dentist contact email extractor** workflows) --- ### 📝 Input Provide input via an `input.json` file. Example structure: ```json { "googleMapsSearchTerm": "Ada Dentists", "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 scraper (the example default is `Ada Dentists`). | | `googleMapsLocation` | Yes | The target geographic location(s) the scraper should focus on (provided as an array of strings). | | `maxBusinesses` | No | Maximum number of businesses to target. The scraper stops when this target is reached (allowed range: `1-1000`). | | `scrapeMaxBusinessesPerLocation` | No | If enabled, the scraper collects up to `maxBusinesses` results per location. If disabled, it combines all locations up to a single total limit. | | `proxyConfiguration` | No | Proxy settings for scraping. Recommended for larger runs; the UI prefill enables `proxy support: true`. | | `proxyConfiguration.proxy support` | No | When set to `true`, enables Apify Proxy for scraping. | *** ### 📦 Output The actor saves **business contact data** to the **default Apify dataset** in JSON format, with **one row per extracted email** when emails are found. #### Example output item ```json { "name": "Example Dental Practice", "website": "https://example-dental.com", "phone": "+1 555 123 4567", "full_address": "123 Main St Example City ST 12345 US", "city": "Example City", "state": "ST", "zip": "12345", "country_code": "US", "scraped_emails": [], "scraped_phones": [], "scraped_social_media": [], "emails_found": 0, "pages_scraped": 0, "avg_rating": 4.6, "total_reviews": 120, "lat": 40.7128, "long": -74.006, "place_id": "PLACE_ID_HERE", "scrape_status": "success", "email_found": "" } ``` > Note: When emails are found, the actor creates a flattened row and uses `email_found` for each email address. In those rows, `scraped_emails` is removed from the pushed record. #### Output Fields | Field | Type | Description | |---|---|---| | `name` | string | Business name | | `website` | string | Business website (saved as provided) | | `phone` | string | Business phone number from the business listing | | `full_address` | string | Full formatted address string | | `city` | string | City name | | `state` | string | State/region | | `zip` | string | ZIP/postal code | | `country_code` | string | Country code | | `scraped_emails` | array | Emails scraped from the business website (present in some dataset rows, omitted in flattened `email_found` rows) | | `scraped_phones` | array | Phone numbers extracted from the business website | | `scraped_social_media` | array | Social media links extracted from the business website | | `emails_found` | number | Count of scraped email results for the business | | `pages_scraped` | number | Number of processed URLs during website scraping | | `avg_rating` | number | Average rating from the business listing | | `total_reviews` | number | Total reviews count from the business listing | | `lat` | number | Latitude | | `long` | number | Longitude | | `place_id` | string | Place identifier from the business listing | | `scrape_status` | string | Scrape outcome status (e.g., `success`, `failed`, `no_website`, or `error`) | | `email_found` | string | The flattened email value pushed into the dataset for each extracted email (empty string when no website/email is saved) | | `scrape_error` | string | Error message when `scrape_status` indicates failure/error | *** ### 🧭 How to use Ada Dentists Email Scraper (via Apify Console) 1. **Open Apify Console**: Go to [console.apify.com](https://console.apify.com) and log in. 2. **Find the actor**: Search for **Ada Dentists Email Scraper** in the Actors tab. 3. **Enter input settings**: In the INPUT panel, provide: - `googleMapsSearchTerm` (e.g., `Ada Dentists`) - `googleMapsLocation` as an array (e.g., `["New York"]`) - Optional tuning: `maxBusinesses`, `scrapeMaxBusinessesPerLocation`, and `proxyConfiguration` 4. **Choose proxy settings (optional but recommended)**: If you’re targeting a larger lead volume, enable `proxyConfiguration.proxy support`. 5. **Start the run**: Click **Run** to begin. Watch the logs to see progress updates for locations and scraping stages. 6. **Review results**: When the run completes, open the dataset (output named in the actor metadata as **Business Contact Data**). 7. **Export your list**: Export the dataset to JSON/CSV from the Apify dataset UI for **dental marketing email list** use cases. No coding required — get accurate results in minutes with this **Ada dentists email scraper**. *** ### 🚀 Advanced features & SEO optimization - **Primary keyword intent for local lead building**: Built for “Ada dentists email scraper” workflows like **dentist lead generation email** and **Ada dental office email finder** campaigns. - **Location-first targeting**: Uses `googleMapsLocation` to focus on the right market area(s) for **local dentist email leads**. - **Flattened outreach-friendly dataset**: When emails are found, each pushed row includes `email_found`, which fits outreach tools and **B2B dentist email database** pipelines. - **Compliance-minded sourcing**: Extracts contact info from publicly available sources and websites—use responsibly for legitimate outreach. *** ### 🎯 Best use cases - 📈 **Marketing teams building local lists**: Generate an **Ada OK dentist email list** for **Ada dental clinic email outreach** in one run - 🧑‍🔬 **Researchers & analysts**: Combine `avg_rating`, `total_reviews`, `lat`, `long`, and `scrape_status` to study dental market density - ✉️ **Cold email operators**: Turn `email_found` rows into a cleaned lead table for **dentist lead generation email** sequences - 🏥 **Practice growth & business development**: Find contact details quickly for targeted follow-ups with practices discovered in Ada-focused searches - 💼 **Sales enablement**: Export `name`, `website`, `phone`, and `scraped_social_media` to enrich CRM records - 🛠️ **Data pipelines & automation**: Use the dataset output fields (`emails_found`, `pages_scraped`, `scrape_status`) to automate QA and prioritization for **dentist contact email extractor** workflows *** ### 🔧 Technical specifications **Supported Input Formats** - ✅ `googleMapsSearchTerm` (string) - ✅ `googleMapsLocation` (array of strings) - ✅ `maxBusinesses` (integer, `1-1000`) - ✅ `scrapeMaxBusinessesPerLocation` (boolean) - ✅ `proxyConfiguration` (object with `proxy support`) **Proxy Support** - ✅ `proxyConfiguration.proxy support` supported via the actor input schema **Retry Mechanism** - ✅ The actor uses a resilient scraping pipeline with fallbacks and stopping conditions (details like exact retry counts are handled internally) **Dataset Structure** - ✅ Saved to the Apify dataset titled **Business Contact Data** - ✅ One row per `email_found` when emails are present - ✅ Includes email/phone/social fields plus business and listing metadata **Rate Limits & Performance** - ✅ Managed via internal concurrency controls and request pacing (high-level behavior) **Limitations** - ❌ Businesses without a website can’t produce website-derived email results - ❌ Extracted emails are based on what’s available on the public business website content *** ### ❓ FAQ #### What does Ada Dentists Email Scraper extract? ✅ It searches for dental businesses in your chosen `googleMapsLocation` and extracts contact information from their websites, including emails (`scraped_emails` / `email_found`), phone numbers (`scraped_phones`), and social media profiles (`scraped_social_media`), alongside business listing fields like `name`, `website`, `phone`, `full_address`, `avg_rating`, and `total_reviews`. #### Do I need to write code to get results? ✅ No. You can run it directly in Apify Console, then export the dataset to JSON/CSV. #### Will it save businesses that have no emails? ✅ It can, depending on the pipeline mode used during the run. Dataset rows include `scrape_status` and counts like `emails_found`, and the actor can also operate in an email-only style so you focus on contactable leads. #### How do I control how many dentists/practices it finds? ✅ Use `maxBusinesses` to set the target cap. If you have multiple locations, `scrapeMaxBusinessesPerLocation` controls whether the cap applies per location or across all locations combined. #### Can I use proxies for higher-volume scraping? ✅ Yes. Provide `proxyConfiguration` (including `proxyConfiguration.proxy support`) to enable proxy support for more reliable scraping at scale. #### What does `scrape_status` mean in the dataset? ✅ `scrape_status` reflects the outcome for each business row. You’ll see values such as `success`, `failed`, `no_website`, or `error`, and failures may include `scrape_error`. #### Can I integrate the output into my CRM? ✅ Yes. The dataset is structured with consistent fields (e.g., `name`, `website`, `phone`, `full_address`, `email_found`) so you can map columns directly in your CRM or spreadsheet workflows. *** ### 🤝 Support & feature requests Have ideas to improve Ada Dentists Email Scraper for **dental practice email scraper Ada** workflows? Tell us what you need. - 💡 **Feature Requests**: Examples include stronger CSV export options, additional field mappings for CRM imports, or improved dataset flattening for specific outreach formats. - 📧 **Contact**: Email Your feedback helps shape what we build next for this **Ada Dentists Email Scraper**. *** ### ✨ Closing CTA / Final thoughts *If you want a practical, SEO-optimized way to build an Ada dentist contact list, Ada Dentists Email Scraper is built for exactly that—at scale and with structured output.*\ Start your run today and turn publicly available dental business data into actionable **Ada dentists email scraper** leads. *** ### Disclaimer **This tool accesses publicly accessible sources**. It does not access private profiles, authenticated data, or password-protected content. You are responsible for complying with applicable laws and regulations (including GDPR/CCPA where relevant), spam and marketing rules, and each website’s Terms of Service. For data removal requests, contact . Use Ada Dentists 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": "Ada Dentists", "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": "Ada Dentists", "googleMapsLocation": [ "New York" ], "maxBusinesses": 5, "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("scraperoka/ada-dentists-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": "Ada Dentists", "googleMapsLocation": ["New York"], "maxBusinesses": 5, "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("scraperoka/ada-dentists-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": "Ada Dentists", "googleMapsLocation": [ "New York" ], "maxBusinesses": 5, "proxyConfiguration": { "useApifyProxy": true } }' | apify call scraperoka/ada-dentists-email-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scraperoka/ada-dentists-email-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Ada Dentists Email Scraper", "description": "📧 Ada Dentists Email Scraper extracts verified dentist emails from Ada Dentists listings. Fast, accurate, and GDPR-friendly*—perfect for B2B outreach, lead generation, and marketing campaigns. 🚀📈 Get targeted dental contacts in minutes!", "version": "1.0", "x-build-id": "ZLbSo3HuHzvgMjIIg" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scraperoka~ada-dentists-email-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scraperoka-ada-dentists-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scraperoka~ada-dentists-email-scraper/runs": { "post": { "operationId": "runs-sync-scraperoka-ada-dentists-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scraperoka~ada-dentists-email-scraper/run-sync": { "post": { "operationId": "run-sync-scraperoka-ada-dentists-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": "Ada Dentists" }, "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 } } } } } } } } } } ```