# Instructure Resource Info Parser Spider (`getdataforme/instructure-resource-info-parser-spider`) Actor The Instructure Resource Info Parser Spider is a web scraping tool designed for extracting detailed information from Instructure's product resources.... - **URL**: https://apify.com/getdataforme/instructure-resource-info-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 --- ## Instructure Resource Info Parser Spider ### Introduction The Instructure Resource Info Parser Spider is a powerful web scraping tool designed to extract detailed information from Instructure's product resources. It efficiently gathers data such as titles, descriptions, video links, and related content, providing valuable insights for various applications. ### Features - **Comprehensive Data Extraction**: Captures essential details including titles, descriptions, and video links. - **High-Quality Data**: Ensures reliable and accurate information retrieval from Instructure's resources. - **Customizable Limits**: Allows setting item limits to control the volume of data scraped per run. - **Flexible URL Configuration**: Supports multiple URLs for targeted scraping operations. - **Seamless Integration**: Easily integrates with Apify Store for streamlined workflows. ### Input Parameters | Parameter | Type | Required | Description | Example | |---------------|---------|----------|-----------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------| | ProductUrls | array | Yes | The product URLs for the spider. Must be valid HTTP/HTTPS links. | `["https://www.instructure.com/resources/webinars/new-next-showcase-next-generation-canvas-and-beyond"]` | | item_limit | integer | No | Maximum items to scrape per actor run. Set to 0 for no limit. | `10` | ### Example Usage #### Input JSON ```json { "ProductUrls": [ "https://www.instructure.com/resources/webinars/new-next-showcase-next-generation-canvas-and-beyond", "https://www.instructure.com/resources/videos/canvas-nextigniteai-agent-demo-video?utm_source=google&utm_medium=performance_max&utm_campaign=2026_01_no_bus_cc_bg_canvas_perfmax&gad_source=1&gad_campaignid=22202059662&gbraid=0AAAAADnEIBqsXYsMrKKQZKrJ9-teQzspP&gclid=CjwKCAjwhqfPBhBWEiwAZo196oMf8sJdH3UmtKE4LRMoh4K_77wpyKtwwJ1-t2-9L5rc4qfOVjDghxoC8CgQAvD_BwE" ], "item_limit": 10 } ```` #### Output JSON ```json [ { "title": "New & Next: The Next Generation of Canvas", "description": "Explore AI, analytics, video, and engagement tools in a simplified Canvas tiered structure, plus new workflows and Impact innovations.", "video_link": "https://fast.wistia.com/embed/medias/4fdf7zxvzb", "related_contents": [ { "title": "Turn Your Assessment Data Into a Forward Looking Map", "url": "https://www.instructure.com/resources/videos/turn-your-assessment-data-forward-looking-map", "resource_type": "Product Demos & Videos" }, { "title": "Canvas Next/IgniteAI Agent Demo Video", "url": "https://www.instructure.com/resources/videos/canvas-nextigniteai-agent-demo-video", "resource_type": "Product Demos & Videos" }, { "title": "New & Next: The Next Generation of Canvas", "url": "https://www.instructure.com/resources/webinars/new-next-showcase-next-generation-canvas-and-beyond", "resource_type": "On-Demand Webinars" } ], "actor_id": "bYS0mbUAI3NCOkyZS", "run_id": "Qdr4vkoLgP0hTnHER" }, { "title": "Canvas Next/IgniteAI Agent Demo Video", "description": "Instructure's team demonstrates the Ignite AI agent, a key component of their Canvas Next tier that integrates AI tools directly into the LMS.", "video_link": "https://fast.wistia.com/embed/medias/57a83mbrj3", "related_contents": [ { "title": "Turn Your Assessment Data Into a Forward Looking Map", "url": "https://www.instructure.com/resources/videos/turn-your-assessment-data-forward-looking-map", "resource_type": "Product Demos & Videos" }, { "title": "Canvas Next/IgniteAI Agent Demo Video", "url": "https://www.instructure.com/resources/videos/canvas-nextigniteai-agent-demo-video", "resource_type": "Product Demos & Videos" }, { "title": "New & Next: The Next Generation of Canvas", "url": "https://www.instructure.com/resources/webinars/new-next-showcase-next-generation-canvas-and-beyond", "resource_type": "On-Demand Webinars" } ], "actor_id": "bYS0mbUAI3NCOkyZS", "run_id": "Qdr4vkoLgP0hTnHER" } ] ``` ### Use Cases - **Market Research and Analysis**: Gather insights into Instructure's product offerings. - **Competitive Intelligence**: Monitor competitor resources for strategic planning. - **Price Monitoring**: Track pricing changes in educational tools. - **Content Aggregation**: Compile educational content for analysis or distribution. - **Academic Research**: Support studies on digital learning platforms. - **Business Automation**: Automate data collection for operational efficiency. ### Installation and Usage 1. Search for "Instructure Resource Info 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 containing objects with the following fields: - **title**: The title of the resource. - **description**: A brief description of the resource. - **video\_link**: URL to an associated video. - **related\_contents**: An array of related resources, each with: - **title**: Title of the related content. - **url**: Link to the related content. - **resource\_type**: Type of the related content (e.g., Product Demos & Videos). - **actor\_id**: Unique identifier for the actor run. - **run\_id**: Unique identifier for the specific run. ### 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 ## `ProductUrls` (type: `array`): The product urls 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 { "ProductUrls": [ "https://www.instructure.com/resources/webinars/new-next-showcase-next-generation-canvas-and-beyond", "https://www.instructure.com/resources/videos/canvas-nextigniteai-agent-demo-video?utm_source=google&utm_medium=performance_max&utm_campaign=2026_01_no_bus_cc_bg_canvas_perfmax&gad_source=1&gad_campaignid=22202059662&gbraid=0AAAAADnEIBqsXYsMrKKQZKrJ9-teQzspP&gclid=CjwKCAjwhqfPBhBWEiwAZo196oMf8sJdH3UmtKE4LRMoh4K_77wpyKtwwJ1-t2-9L5rc4qfOVjDghxoC8CgQAvD_BwE" ], "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/instructure-resource-info-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/instructure-resource-info-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/instructure-resource-info-parser-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/instructure-resource-info-parser-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Instructure Resource Info Parser Spider", "description": "The Instructure Resource Info Parser Spider is a web scraping tool designed for extracting detailed information from Instructure's product resources....", "version": "0.0", "x-build-id": "3YXb9em4suvzakPG7" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~instructure-resource-info-parser-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-instructure-resource-info-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~instructure-resource-info-parser-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-instructure-resource-info-parser-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~instructure-resource-info-parser-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-instructure-resource-info-parser-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "ProductUrls": { "title": "Product Urls", "minItems": 1, "type": "array", "description": "The product urls for the spider.", "default": [ "https://www.instructure.com/resources/webinars/new-next-showcase-next-generation-canvas-and-beyond", "https://www.instructure.com/resources/videos/canvas-nextigniteai-agent-demo-video?utm_source=google&utm_medium=performance_max&utm_campaign=2026_01_no_bus_cc_bg_canvas_perfmax&gad_source=1&gad_campaignid=22202059662&gbraid=0AAAAADnEIBqsXYsMrKKQZKrJ9-teQzspP&gclid=CjwKCAjwhqfPBhBWEiwAZo196oMf8sJdH3UmtKE4LRMoh4K_77wpyKtwwJ1-t2-9L5rc4qfOVjDghxoC8CgQAvD_BwE" ], "items": { "type": "string" } }, "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 } } } } } } } } } } ```