# Appranker Category Parser Spider (`getdataforme/appranker-category-parser-spider`) Actor The Appranker Category Parser Spider is an Apify Actor that automates scraping detailed app data from Appranker, including names, descriptions, ratings, and metadata.... - **URL**: https://apify.com/getdataforme/appranker-category-parser-spider.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** AI, Automation, E-commerce - **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 The Appranker Category Parser Spider is an Apify Actor that automates scraping detailed app data from Appranker, including names, descriptions, ratings, and metadata.... --- ### PART 1: Generate README.md ## Appranker Category Parser Spider The Appranker Category Parser Spider is a powerful Apify Actor designed to scrape and extract detailed product information from Appranker, a platform showcasing apps and tools across various categories. It enables users to gather comprehensive data on apps, including descriptions, ratings, and metadata, making it ideal for market research, competitive analysis, and data-driven decision-making. By automating the extraction process, this Actor saves time and ensures access to up-to-date, structured information from Appranker's listings. ### Features - **Comprehensive Data Extraction**: Pulls detailed app information such as names, taglines, descriptions, website URLs, logos, ratings, and more from Appranker categories. - **High Reliability**: Built with robust error handling to manage dynamic web content and ensure consistent data retrieval. - **Scalable Performance**: Optimized for handling large volumes of data efficiently, with support for batch processing and minimal resource usage. - **Structured Output**: Delivers clean, JSON-formatted results that integrate seamlessly with downstream tools like databases or analytics platforms. - **No Input Configuration Needed**: Runs out-of-the-box without requiring custom parameters, simplifying setup for quick deployments. - **Real-Time Insights**: Captures live data including view counts, approval status, and creation dates for timely analysis. - **Apify Integration**: Leverages Apify's infrastructure for easy scheduling, monitoring, and exporting in multiple formats (JSON, CSV, Excel). ### Input Parameters | Parameter | Type | Required | Description | Example | |-----------|------|----------|-------------|---------| | N/A | N/A | N/A | No input parameters are required for this Actor. It operates with default settings to scrape Appranker categories automatically. | N/A | ### Example Usage To run the Appranker Category Parser Spider, simply start it on the Apify platform. Below is an example of the output JSON structure: **Example Input (No input required):** ```json {} ```` **Example Output:** ```json [ { "id": "69d89b5c9d350ca729303c05", "name": "ProProfs Knowledge Base", "tagline": "ProProfs Knowledge Base is a comprehensive cloud-based repository designed to store, access, and share information seamlessly. This platform empowers organizations to create a centralized hub for thei", "description": "ProProfs Knowledge Base is a comprehensive cloud-based repository designed to store, access, and share information seamlessly. This platform empowers organizations to create a centralized hub for their knowledge, enabling teams to easily find and utilize essential resources, manuals, and internal documentation. With its user-friendly interface, ProProfs Knowledge Base allows users to organize content efficiently, ensuring that information is readily available to employees and customers alike.\n\nKey features include customizable templates, robust search functionality, and the ability to categorize articles for quick retrieval. The platform also supports collaboration, allowing team members to contribute and update content, fostering a culture of knowledge sharing. Additionally, ProProfs Knowledge Base integrates with other ProProfs tools, enhancing its functionality and providing a unified experience for training, support, and customer engagement. This makes it an invaluable asset for businesses looking to improve efficiency, reduce redundancy, and enhance customer satisfaction through easy access to vital information.", "website_url": "http://www.proprofs.com", "logo_url": "https://ph-files.imgix.net/5188a779-02f0-4653-acfd-872a6aae4199.png?auto=compress&codec=mozjpeg&cs=strip&auto=format&w=64&h=64&fit=crop&frame=1", "has_valid_logo": true, "status": "approved", "FirstSourceOfApp": "Product Hunt", "created_date": "2026-04-10T06:40:28.009000", "updated_date": "2026-04-10T06:40:28.009000", "views": 0, "average_rating": null, "total_reviews": 0, "categories": [], "actor_id": "0RyQzDWbu1qlmNCFQ", "run_id": "OpllqQ0DfrK8mdrtN" }, { "id": "69d3fe277d719f10be60d6c6", "name": "HiMama", "tagline": "HiMama is an innovative platform designed to enhance the experience of early childhood education by allowing parents and educators to record, share, and relive precious moments in a child's developmen", "description": "HiMama is an innovative platform designed to enhance the experience of early childhood education by allowing parents and educators to record, share, and relive precious moments in a child's development. The app facilitates seamless communication between educators and families, ensuring that parents stay informed about their child's daily activities and milestones. With features that include easy-to-use documentation tools, parents can receive real-time updates, photos, and videos, fostering a deeper connection with their child's learning journey.\n\nAdditionally, HiMama offers a comprehensive suite of management tools for childcare centers, streamlining operations and enhancing program quality. Educators benefit from ready-to-use, developmentally appropriate lesson plans aligned with educational standards, while the platform also supports billing and payment processes, making financial management straightforward for both parents and center administrators. By prioritizing family engagement and professional development, HiMama stands out as a holistic solution that not only enriches the educational experience but also strengthens the bond between families and educators.", "website_url": "http://himama.com", "logo_url": "https://ph-files.imgix.net/922bbe03-7115-47e0-8703-dac1d4ee7b23.png?auto=compress&codec=mozjpeg&cs=strip&auto=format&w=64&h=64&fit=crop&frame=1", "has_valid_logo": true, "status": "approved", "FirstSourceOfApp": "Product Hunt", "created_date": "2026-04-06T18:40:39.043000", "updated_date": "2026-04-06T18:40:39.043000", "views": 0, "average_rating": null, "total_reviews": 0, "categories": [], "actor_id": "0RyQzDWbu1qlmNCFQ", "run_id": "OpllqQ0DfrK8mdrtN" }, { "id": "69d89b598f19f3f143d8b083", "name": "HelpDocs", "tagline": "HelpDocs is an AI-enabled knowledge base platform designed to empower businesses to create a centralized hub of documentation for their customers and support teams. With its intuitive editor, HelpDocs", "description": "HelpDocs is an AI-enabled knowledge base platform designed to empower businesses to create a centralized hub of documentation for their customers and support teams. With its intuitive editor, HelpDocs allows teams to effortlessly build and manage comprehensive documentation, ensuring that users have access to accurate and up-to-date information. The platform features intelligent search capabilities that provide customers with quick, relevant answers based on their queries, significantly reducing the volume of support tickets.\n\nHelpDocs stands out with its professional templates that enable businesses to go live in just hours, making it easy to launch a knowledge base without extensive technical expertise. The platform also offers robust analytics tools, allowing organizations to track engagement and optimize their documentation based on user interactions. By streamlining the support process and enhancing customer self-service, HelpDocs helps companies scale their operations efficiently while maintaining high levels of customer satisfaction.", "website_url": "https://www.helpdocs.io", "logo_url": "https://ph-files.imgix.net/ad4f780c-e9b0-4454-b39c-787de84cb805.png?auto=compress&codec=mozjpeg&cs=strip&auto=format&w=64&h=64&fit=crop&frame=1", "has_valid_logo": true, "status": "approved", "FirstSourceOfApp": "Product Hunt", "created_date": "2026-04-10T06:40:25.606000", "updated_date": "2026-04-10T06:40:25.606000", "views": 0, "average_rating": 5, "total_reviews": 0, "categories": [], "actor_id": "0RyQzDWbu1qlmNCFQ", "run_id": "OpllqQ0DfrK8mdrtN" } ] ``` ### Use Cases - **Market Research and Analysis**: Gather data on trending apps in specific categories to identify opportunities and trends. - **Competitive Intelligence**: Monitor competitors' app listings, ratings, and features for strategic insights. - **Price Monitoring**: Track app-related services or tools for pricing strategies in SaaS markets. - **Content Aggregation**: Build datasets of app descriptions and metadata for content creation or SEO purposes. - **Academic Research**: Collect structured data on educational or productivity tools for studies on technology adoption. - **Business Automation**: Automate data feeds into CRM systems or dashboards for real-time app market overviews. ### Installation and Usage 1. Search for "Appranker Category Parser Spider" in the Apify Store. 2. Click "Try for free" or "Run". 3. Configure input parameters (none required). 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 Actor outputs a JSON array of objects, each representing an app from Appranker. Key fields include: - `id`: Unique identifier for the app. - `name`: App name. - `tagline`: Short promotional description. - `description`: Detailed app overview. - `website_url`: Official website link. - `logo_url`: URL to the app's logo image. - `has_valid_logo`: Boolean indicating logo validity. - `status`: Approval status (e.g., "approved"). - `FirstSourceOfApp`: Original source (e.g., "Product Hunt"). - `created_date` and `updated_date`: Timestamps in ISO format. - `views`, `average_rating`, `total_reviews`: Engagement metrics. - `categories`: Array of associated categories (may be empty). - `actor_id` and `run_id`: Metadata from the Apify run. This structured format ensures easy parsing and integration. ### 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! *** ### PART 2: Generate Description (STRICT: 280-295 characters ONLY) # Actor input Schema ## Actor input object example ```json {} ``` # Actor output Schema ## `results` (type: `string`): Scraped data items from dataset # 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-category-parser-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-category-parser-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-category-parser-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/appranker-category-parser-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Appranker Category Parser Spider", "description": "The Appranker Category Parser Spider is an Apify Actor that automates scraping detailed app data from Appranker, including names, descriptions, ratings, and metadata....", "version": "0.0", "x-build-id": "UBLUVje3OhJ7fmjhJ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~appranker-category-parser-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-appranker-category-parser-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~appranker-category-parser-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-appranker-category-parser-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~appranker-category-parser-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-appranker-category-parser-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": {} }, "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 } } } } } } } } } } ```