# Goconqr Discovery Parser Spider (`getdataforme/goconqr-discovery-parser-spider`) Actor The Goconqr Discovery Parser Spider efficiently extracts educational resources from Goconqr using keywords, customizable limits, and high-quality data extraction. It supports research, content discovery, and automation with user-friendly configuration for streamlined academic and market analysis. - **URL**: https://apify.com/getdataforme/goconqr-discovery-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 --- ## Goconqr Discovery Parser Spider ### Introduction The Goconqr Discovery Parser Spider is a powerful tool designed to extract valuable educational resources from the Goconqr platform. It enables users to efficiently gather data on mind maps, slide sets, and other learning materials based on specified keywords, facilitating research and content discovery. ### Features - **Keyword-Based Scraping**: Extracts resources using user-defined keywords. - **Customizable Page Limits**: Control the number of pages to scrape for comprehensive data collection. - **Flexible Item Limit**: Set a maximum number of items per run or allow unlimited scraping. - **High-Quality Data Extraction**: Ensures accurate and reliable data retrieval from Goconqr. - **Efficient Performance**: Optimized for speed and resource management during extraction. - **User-Friendly Configuration**: Simple setup with intuitive input parameters. ### Input Parameters | Parameter | Type | Required | Description | Example | |-------------|---------|----------|-----------------------------------------------------------------------------|------------------| | Keywords | Array | No | The keywords for the spider. | `["History"]` | | Max Pages | Integer | No | The maximum number of pages to scrape. | `1` | | Item Limit | Integer | No | Maximum items to scrape per actor run; set to 0 for no limit. | `10` | ### Example Usage #### Input JSON ```json { "Keywords": ["History"], "MaxPages": 2, "item_limit": 20 } ```` #### Output JSON ```json [ { "resource_name": "History of Medicine: Ancient Ideas", "writer_name": "James McConnell", "rating_count": "4.13", "no_of_reviews": 16, "category": "Resources", "resource_type": "Mind Map", "resource_url": "https://www.goconqr.com/mindmap/729702/history-of-medicine-ancient-ideas", "view_count": 3321, "pin_count": 108, "tag_list": [ "gcse", "gcse history of medicine", "history" ], "updated_at": "2019-05-04T05:25:24Z", "search_keyword": "History", "page": 1, "actor_id": "NRJpvcDlQYxuthndm", "run_id": "l3NjVVUXjy2ghz1lK" }, { "resource_name": "Using GoConqr to study History", "writer_name": "Sarah Egan", "rating_count": "4.18", "no_of_reviews": 28, "category": "Resources", "resource_type": "Slide Set", "resource_url": "https://www.goconqr.com/slide/3960243/using-goconqr-to-study-history", "view_count": 1481, "pin_count": 35, "tag_list": [ "secondary", "second", "gcse", "a level", "leaving", "junior", "history" ], "updated_at": "2017-10-26T20:02:21Z", "search_keyword": "History", "page": 1, "actor_id": "NRJpvcDlQYxuthndm", "run_id": "l3NjVVUXjy2ghz1lK" }, { "resource_name": "New GCSE history content", "writer_name": "Sarah Egan", "rating_count": "3.71", "no_of_reviews": 7, "category": "Resources", "resource_type": "Mind Map", "resource_url": "https://www.goconqr.com/mindmap/4190703/new-gcse-history-content", "view_count": 1066, "pin_count": 23, "tag_list": [ "new gcse", "history", "overview" ], "updated_at": "2018-04-12T11:17:05Z", "search_keyword": "History", "page": 1, "actor_id": "NRJpvcDlQYxuthndm", "run_id": "l3NjVVUXjy2ghz1lK" } ] ``` ### Use Cases - **Market Research and Analysis**: Gather data on educational trends and popular topics. - **Competitive Intelligence**: Monitor competitor content strategies and offerings. - **Price Monitoring**: Track pricing changes in educational resources. - **Content Aggregation**: Compile comprehensive datasets for analysis or reporting. - **Academic Research**: Support studies with curated educational materials. - **Business Automation**: Automate data collection processes for efficiency. ### Installation and Usage 1. Search for "Goconqr Discovery Parser Spider" in the Apify Store. 2. Click "Try for free" or "Run". 3. Configure input parameters as needed. 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 a JSON array of objects, each representing a resource with fields such as `resource_name`, `writer_name`, `rating_count`, `no_of_reviews`, `category`, `resource_type`, `resource_url`, `view_count`, `pin_count`, `tag_list`, `updated_at`, `search_keyword`, `page`, `actor_id`, and `run_id`. ### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: [Contact Us](https://getdataforme.com/contact/) We're here to help you get the most out of this Actor! # Actor input Schema ## `Keywords` (type: `array`): The keywords for the spider. ## `MaxPages` (type: `integer`): The max pages for the spider. ## `item_limit` (type: `integer`): Maximum items to scrape per actor run. Set to 0 for no limit. ## Actor input object example ```json { "Keywords": [ "History" ], "MaxPages": 1, "item_limit": 10 } ``` # 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/goconqr-discovery-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/goconqr-discovery-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/goconqr-discovery-parser-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/goconqr-discovery-parser-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Goconqr Discovery Parser Spider", "description": "The Goconqr Discovery Parser Spider efficiently extracts educational resources from Goconqr using keywords, customizable limits, and high-quality data extraction. It supports research, content discovery, and automation with user-friendly configuration for streamlined academic and market analysis.", "version": "0.0", "x-build-id": "dhMrQ7izmy2JAaEwL" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~goconqr-discovery-parser-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-goconqr-discovery-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~goconqr-discovery-parser-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-goconqr-discovery-parser-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~goconqr-discovery-parser-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-goconqr-discovery-parser-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "Keywords": { "title": "Keywords", "type": "array", "description": "The keywords for the spider.", "default": [ "History" ], "items": { "type": "string" } }, "MaxPages": { "title": "Max Pages", "minimum": 1, "type": "integer", "description": "The max pages for the spider.", "default": 1 }, "item_limit": { "title": "Item limit", "minimum": 0, "type": "integer", "description": "Maximum items to scrape per actor run. Set to 0 for no limit.", "default": 10 } } }, "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 } } } } } } } } } } ```