# Appranker Categories Spider (`getdataforme/appranker-categories-spider`) Actor Appranker Categories Spider is an Apify Actor that scrapes comprehensive app data from AppRanker categories, including names, descriptions, categories, tags, and FAQs.... - **URL**: https://apify.com/getdataforme/appranker-categories-spider.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $9.00 / 1,000 results 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 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 ### Description Appranker Categories Spider is an Apify Actor that scrapes comprehensive app data from AppRanker categories, including names, descriptions, categories, tags, and FAQs.... --- ## Appranker Categories Spider ### Introduction The Appranker Categories Spider is a powerful Apify Actor designed to scrape and extract detailed information about apps from specific categories on AppRanker. It automates the collection of app data, including names, descriptions, categories, tags, and more, enabling users to gather insights for market research, competitive analysis, and business intelligence. This tool simplifies the process of accessing structured app data from AppRanker's ExploreV2 pages, delivering reliable and comprehensive results in JSON format. ### Features - **Comprehensive Data Extraction**: Captures key app details such as name, tagline, description, logo URL, website, categories, tags, and FAQs. - **Flexible URL Input**: Supports multiple input URLs for scraping different categories or pages. - **High Reliability**: Built on Apify's robust infrastructure, ensuring consistent performance and data accuracy. - **JSON Output**: Produces clean, structured JSON data suitable for integration with other tools or databases. - **Scalable and Fast**: Handles large volumes of data efficiently, with options for monitoring and exporting results. - **Error Handling**: Includes built-in mechanisms to manage scraping failures and provide informative logs. - **No Coding Required**: User-friendly interface for easy configuration and execution. ### Input Parameters | Parameter | Type | Required | Description | Example | |-----------|------|----------|-------------|---------| | InputUrls | array | Yes | An array of URLs pointing to AppRanker category pages to scrape. Each URL must be a valid HTTP or HTTPS link. | ["https://appranker.io/ExploreV2?CategoryTagClicked=Design"] | ### Example Usage #### Input JSON ```json { "InputUrls": [ "https://appranker.io/ExploreV2?CategoryTagClicked=Design" ] } ```` #### Output JSON ```json [ { "id": "697d693de7e629bf3475e92e", "name": "Salesai", "tagline": "Salesai is an advanced sales forecasting tool that leverages artificial intelligence...", "description": "Salesai is an advanced sales forecasting tool that leverages artificial intelligence...", "logo_url": "https://ph-files.imgix.net/59310f45-1696-4172-8c40-c664011eac9a.png?auto=format", "has_valid_logo": true, "video_url": null, "website_url": "https://salesai.kamingo.in", "screenshots": [], "faq": [ { "question": "What is Salesai?", "answer": "Salesai is an advanced sales forecasting tool..." } ], "categories": ["AI", "Sales", "Analytics"], "tags": ["AI-powered sales forecasting", "real-time sales analysis"], "status": "approved", "created_date": "2026-01-31T02:30:21.638000", "updated_date": "2026-02-23T13:41:18.749000" } ] ``` ### Use Cases - **Market Research**: Analyze trends in app categories to identify popular niches and emerging technologies. - **Competitive Intelligence**: Gather data on competitors' apps, including features, pricing, and user reviews. - **Content Aggregation**: Collect app descriptions and FAQs for content creation or database building. - **Academic Research**: Study app ecosystems, categories, and user engagement metrics. - **Business Automation**: Automate data collection for reports on app performance and market share. - **Price Monitoring**: Track pricing details and billing cycles for apps in specific categories. ### Installation and Usage 1. Search for "Appranker Categories Spider" in the Apify Store. 2. Click "Try for free" or "Run". 3. Configure input parameters by providing the desired URLs. 4. Click "Start" to begin extraction. 5. Monitor progress in the log. 6. Export results in your preferred format (JSON, CSV, Excel). ### Output Format The output is an array of JSON objects, each representing an app from the scraped category. Key fields include: - `id`: Unique identifier for the app. - `name`: App name. - `description`: Full description. - `categories`: List of categories. - `tags`: Associated tags. - `website_url`: Official website link. - `faq`: Array of question-answer pairs. - Additional metadata like creation dates and status. Results are stored in the Actor's dataset for easy access and export. ### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: https://getdataforme.com/contact/ We're here to help you get the most out of this Actor! *** # Actor input Schema ## `InputUrls` (type: `array`): The input urls for the spider. ## Actor input object example ```json { "InputUrls": [ "https://appranker.io/ExploreV2?CategoryTagClicked=Design" ] } ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/appranker-categories-spider").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 = {} # Run the Actor and wait for it to finish run = client.actor("getdataforme/appranker-categories-spider").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 '{}' | apify call getdataforme/appranker-categories-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/appranker-categories-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Appranker Categories Spider", "description": "Appranker Categories Spider is an Apify Actor that scrapes comprehensive app data from AppRanker categories, including names, descriptions, categories, tags, and FAQs....", "version": "0.0", "x-build-id": "BFKDlaNYTgIORhqs2" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~appranker-categories-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-appranker-categories-spider", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~appranker-categories-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-appranker-categories-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~appranker-categories-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-appranker-categories-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "InputUrls": { "title": "Input Urls", "minItems": 1, "type": "array", "description": "The input urls for the spider.", "default": [ "https://appranker.io/ExploreV2?CategoryTagClicked=Design" ], "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 } } } } } } } } } } ```