# Instructure profileinfo Scraper (`getdataforme/instructure-profileinfo-scraper`) Actor
The Instructure profileinfo Scraper extracts detailed metadata from Instructure pages, ideal for market research and content aggregation. It offers comprehensive data extraction, high data quality, efficient performance, and scalability....
- **URL**: https://apify.com/getdataforme/instructure-profileinfo-scraper.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 profileinfo Scraper
The Instructure profileinfo Scraper is a powerful tool designed to extract detailed information from Instructure web pages. This Actor efficiently gathers metadata, titles, descriptions, and more, providing valuable insights for various applications such as market research and content aggregation.
### Features
- **Comprehensive Data Extraction**: Captures a wide range of metadata including titles, descriptions, and structured data.
- **High Data Quality**: Ensures accurate and reliable data collection from Instructure pages.
- **Efficient Performance**: Optimized for fast and efficient data scraping.
- **User-Friendly**: Easy to set up and use, with minimal configuration required.
- **Scalable**: Handles multiple URLs simultaneously, making it suitable for large-scale data collection.
- **Customizable**: Flexible input parameters allow for tailored data extraction.
- **Output Versatility**: Provides data in various formats for easy integration into your workflow.
### Input Parameters
| Parameter | Type | Required | Description | Example |
|------------|--------|----------|----------------------------|-------------------------------------------------------------------------|
| startUrls | array | Yes | URLs to start with. | `["https://www.instructure.com/resources/videos/canvas-career-skills-scale?gad_campaignid=22202059662"]` |
### Example Usage
#### Example Input JSON
```json
{
"startUrls": [
{
"url": "https://www.instructure.com/resources/videos/canvas-career-skills-scale?gad_campaignid=22202059662"
}
]
}
````
#### Example Output JSON
```json
[
{
"url": "https://www.instructure.com/resources/videos/canvas-career-skills-scale?gad_campaignid=22202059662",
"nodeId": "26878",
"title": "Canvas Career: Skills at Scale | Instructure",
"metaDescription": "Discover Canvas Career, the workforce learning platform designed for scalable, skills-based training. Deliver role-aligned learning, track progress with insights, and build the skills that matter\u2014at scale.",
"canonicalUrl": "https://www.instructure.com/resources/videos/canvas-career-skills-scale",
"ogTitle": "Canvas Career: Skills at Scale",
"ogDescription": "Discover Canvas Career, the workforce learning platform designed for scalable, skills-based training. Deliver role-aligned learning, track progress with insights, and build the skills that matter\u2014at scale.",
"h1": "Canvas Career: Skills at Scale",
"description": "Instructure is greater than the sum of its parts: It's where the best edtech, the best partners, and the best community unite.",
"pdfUrl": null,
"hasPdfViewer": false,
"relatedContent": [],
"topics": [],
"structuredData": []
}
]
```
### Use Cases
- **Market Research and Analysis**: Gather insights on educational platforms and trends.
- **Competitive Intelligence**: Monitor competitor content and strategies.
- **Content Aggregation**: Collect and organize educational resources.
- **Academic Research**: Analyze educational content for research purposes.
- **Business Automation**: Automate data collection for business intelligence.
### Installation and Usage
1. Search for "Instructure profileinfo Scraper" in the Apify Store.
2. Click "Try for free" or "Run".
3. Configure input parameters.
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 where each object contains:
- `url`: The URL of the scraped page.
- `nodeId`: Unique identifier for the content node.
- `title`: The page title.
- `metaDescription`: Meta description of the page.
- `canonicalUrl`: Canonical URL of the page.
- `ogTitle`: Open Graph title.
- `ogDescription`: Open Graph description.
- `h1`: Main heading of the page.
- `description`: General description of the content.
- `pdfUrl`: URL to any PDF content, if available.
- `hasPdfViewer`: Boolean indicating if a PDF viewer is present.
- `relatedContent`: Array of related content links.
- `topics`: Array of topics covered.
- `structuredData`: Array of structured data elements.
### Support
For custom/simplified outputs or bug reports, please contact:
- Email: support@getdataforme.com
- Subject line: "custom support"
- Contact form:
We're here to help you get the most out of this Actor!
# Actor input Schema
## `startUrls` (type: `array`):
URLs to start with.
## Actor input object example
```json
{
"startUrls": [
{
"url": "https://www.instructure.com/resources/videos/canvas-career-skills-scale?gad_campaignid=22202059662"
}
]
}
```
# Actor output Schema
## `results` (type: `string`):
No description
# 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 = {
"startUrls": [
{
"url": "https://www.instructure.com/resources/videos/canvas-career-skills-scale?gad_campaignid=22202059662"
}
]
};
// Run the Actor and wait for it to finish
const run = await client.actor("getdataforme/instructure-profileinfo-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 = { "startUrls": [{ "url": "https://www.instructure.com/resources/videos/canvas-career-skills-scale?gad_campaignid=22202059662" }] }
# Run the Actor and wait for it to finish
run = client.actor("getdataforme/instructure-profileinfo-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 '{
"startUrls": [
{
"url": "https://www.instructure.com/resources/videos/canvas-career-skills-scale?gad_campaignid=22202059662"
}
]
}' |
apify call getdataforme/instructure-profileinfo-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=getdataforme/instructure-profileinfo-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Instructure profileinfo Scraper",
"description": "The Instructure profileinfo Scraper extracts detailed metadata from Instructure pages, ideal for market research and content aggregation. It offers comprehensive data extraction, high data quality, efficient performance, and scalability....",
"version": "0.0",
"x-build-id": "DukeuwOIrLzd4hShe"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/getdataforme~instructure-profileinfo-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-getdataforme-instructure-profileinfo-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": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/getdataforme~instructure-profileinfo-scraper/runs": {
"post": {
"operationId": "runs-sync-getdataforme-instructure-profileinfo-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/getdataforme~instructure-profileinfo-scraper/run-sync": {
"post": {
"operationId": "run-sync-getdataforme-instructure-profileinfo-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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",
"properties": {
"startUrls": {
"title": "Start URLs",
"type": "array",
"description": "URLs to start with.",
"items": {
"type": "object",
"required": [
"url"
],
"properties": {
"url": {
"type": "string",
"title": "URL of a web page",
"format": "uri"
}
}
}
}
}
},
"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
}
}
}
}
}
}
}
}
}
}
```