# Minminiolie Category Scraper (`getdataforme/minminiolie-category-scraper`) Actor
Project Cheerio Crawler Typescript is a robust web scraping tool using Cheerio for efficient data extraction from websites. It offers customizable crawling, scalable performance, and versatile output formats like JSON, CSV, and Excel....
- **URL**: https://apify.com/getdataforme/minminiolie-category-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
## Project Cheerio Crawler Typescript
### Introduction
Project Cheerio Crawler Typescript is a powerful web scraping tool designed to extract data from web pages efficiently. Utilizing the Cheerio library, this actor is perfect for gathering structured data from websites, making it an essential tool for data analysts and researchers.
### Features
- **Efficient Data Extraction**: Quickly scrape data from web pages using Cheerio.
- **Customizable Crawling**: Set the maximum number of requests to control the scope of your data collection.
- **High Data Quality**: Ensures accurate and reliable data extraction.
- **Scalable Performance**: Handles large-scale data scraping tasks with ease.
- **User-Friendly Interface**: Simple setup and configuration for immediate use.
- **Versatile Output Formats**: Export data in JSON, CSV, or Excel formats.
- **Robust Error Handling**: Built-in mechanisms to manage and report errors effectively.
### Input Parameters
| Parameter | Type | Required | Description | Example |
|------------|---------|----------|-----------------------------------------------------------|-------------------------------------------------------------------------|
| searchUrl | string | Yes | The starting URL for the crawler. | `https://www.miniolie.com/collections/miniolie-fun-aunt-series` |
| maxItems | integer | No | Maximum number of requests the crawler can make. | `100` |
### Example Usage
#### Example Input JSON
```json
{
"searchUrl": "https://www.miniolie.com/collections/miniolie-fun-aunt-series",
"maxItems": 100
}
````
#### Example Output JSON
```json
[
{
"productId": "16072779287562069377350000",
"formId": "quick-add-main-collection-product-list__16072779287562069377350000",
"title": "DON'T MAKE ME CALL MY AUNT Baby Toddler Sweatshirt",
"url": "https://www.miniolie.com/products/don-t-make-me-call-my-aunt-baby-toddler-sweatshirt_6krr",
"quickAddUrl": "https://www.miniolie.com/products/don-t-make-me-call-my-aunt-baby-toddler-sweatshirt_6krr",
"featuredImage": {
"src": "https://img-va.myshopline.com/image/store/1677748422329/1764126402.jpg?w=800&h=800",
"srcset": "https://img-va.myshopline.com/image/store/1677748422329/1764126402_375x.jpg?w=800&h=800 375w, https://img-va.myshopline.com/image/store/1677748422329/1764126402_540x.jpg?w=800&h=800 540w, https://img-va.myshopline.com/image/store/1677748422329/1764126402_720x.jpg?w=800&h=800 720w, https://img-va.myshopline.com/image/store/1677748422329/1764126402_800x.jpg?w=800&h=800 800w",
"alt": "DON'T MAKE ME CALL MY AUNT Baby Toddler Sweatshirt - Cream - 3-5T - image 1"
},
"badge": null,
"pricing": {
"currency": "$USD",
"salePrice": null,
"originalPrice": 23.95,
"discountPercent": null
},
"colorSwatches": []
}
]
```
### Use Cases
- **Market Research and Analysis**: Gather data on products and pricing for market insights.
- **Competitive Intelligence**: Monitor competitor websites for strategic information.
- **Price Monitoring**: Track price changes and trends over time.
- **Content Aggregation**: Collect and organize content from multiple sources.
- **Academic Research**: Extract data for research projects and studies.
- **Business Automation**: Automate data collection processes for efficiency.
### Installation and Usage
1. Search for "Project Cheerio Crawler Typescript" 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 represents a product with fields such as `productId`, `title`, `url`, `featuredImage`, and `pricing`. This structured format allows for easy data manipulation and analysis.
### 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
## `searchUrl` (type: `string`):
URLs to start with.
## `maxItems` (type: `integer`):
Maximum number of requests that can be made by this crawler.
## Actor input object example
```json
{
"searchUrl": "https://www.miniolie.com/collections/miniolie-fun-aunt-series",
"maxItems": 100
}
```
# 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 = {
"searchUrl": "https://www.miniolie.com/collections/miniolie-fun-aunt-series"
};
// Run the Actor and wait for it to finish
const run = await client.actor("getdataforme/minminiolie-category-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 = { "searchUrl": "https://www.miniolie.com/collections/miniolie-fun-aunt-series" }
# Run the Actor and wait for it to finish
run = client.actor("getdataforme/minminiolie-category-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 '{
"searchUrl": "https://www.miniolie.com/collections/miniolie-fun-aunt-series"
}' |
apify call getdataforme/minminiolie-category-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=getdataforme/minminiolie-category-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Minminiolie Category Scraper",
"description": "Project Cheerio Crawler Typescript is a robust web scraping tool using Cheerio for efficient data extraction from websites. It offers customizable crawling, scalable performance, and versatile output formats like JSON, CSV, and Excel....",
"version": "0.0",
"x-build-id": "MMDf7DZAwJKIEdvJr"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/getdataforme~minminiolie-category-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-getdataforme-minminiolie-category-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~minminiolie-category-scraper/runs": {
"post": {
"operationId": "runs-sync-getdataforme-minminiolie-category-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~minminiolie-category-scraper/run-sync": {
"post": {
"operationId": "run-sync-getdataforme-minminiolie-category-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": {
"searchUrl": {
"title": "Enter the Category Url",
"type": "string",
"description": "URLs to start with."
},
"maxItems": {
"title": "Max Number ",
"type": "integer",
"description": "Maximum number of requests that can be made by this crawler.",
"default": 100
}
}
},
"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
}
}
}
}
}
}
}
}
}
}
```