# Accesscrop Blog Info Parser Spider (`getdataforme/accesscrop-blog-info-parser-spider`) Actor
Accesscorp Blog Info Parser Spider is a web scraping tool that extracts detailed blog post data from Accesscorp's website, outputting structured JSON for easy integration and analysis....
- **URL**: https://apify.com/getdataforme/accesscrop-blog-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, 0 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
---
## Accesscorp Blog Info Parser Spider
### Introduction
Accesscorp Blog Info Parser Spider is a powerful web scraping tool designed to extract detailed information from blog posts on the Accesscorp website. It efficiently parses and organizes data into structured JSON format, providing valuable insights for businesses and researchers.
### Features
- **Comprehensive Data Extraction**: Parses full content of blog articles including titles, URLs, authors, publication dates, and more.
- **High-Quality Data Output**: Ensures reliable and accurate data extraction with minimal errors.
- **Efficient Performance**: Fast processing speeds to handle large volumes of data seamlessly.
- **Structured JSON Format**: Outputs data in a well-organized JSON format for easy integration and analysis.
- **Customizable Parameters**: Allows users to tailor input parameters for specific scraping needs.
### Input Parameters Table
| Parameter | Type | Required | Description | Example |
|-----------|--------|----------|-----------------------------------------------------------------------------|--------------------------------|
| url | String | Yes | The URL of the blog post to be parsed. | `"https://www.accesscorp.com/blog/post-url"` |
### Example Usage
```json
{
"input": {
"url": "https://www.accesscorp.com/blog/post-url"
}
}
````
#### Example Output JSON
```json
{
"status": {
"message": "Actor finished successfully",
"type": "SUCCESS"
},
"requestStats": {
"bytesReceived": 0,
"numRequests": 1,
"numRequestsPerMinute": 1.0,
"numRequestsQueued": 0,
"numRequestsScheduled": 1,
"numRequestsSuccessful": 1
},
"resultItems": [
{
"breadcrumbs": [],
"content": "...
",
"date": "2023-10-05T00:00:00Z",
"description": "",
"image": "https://www.accesscorp.com/wp-content/uploads/2023/10/blog-image.jpg",
"keywords": ["keyword1", "keyword2"],
"lastmod": null,
"og_description": "",
"og_image": "https://www.accesscorp.com/wp-content/uploads/2023/10/blog-image.jpg",
"og_title": "Blog Title",
"og_type": "article",
"og_url": "https://www.accesscorp.com/blog/post-url",
"title": "Blog Title",
"url": "https://www.accesscorp.com/blog/post-url"
}
],
"runStats": {
"endTime": "2023-10-05T12:00:00Z",
"numPageViews": 1,
"numTasksSucceeded": 1,
"startTime": "2023-10-05T11:59:59Z"
},
"suggestedRetries": []
}
```
### Use Cases
- **Market Research and Analysis**: Extract blog data for competitive insights.
- **Competitive Intelligence**: Monitor industry trends through blog content analysis.
- **Price Monitoring**: Track pricing strategies discussed in blogs.
- **Content Aggregation**: Compile information from multiple blog posts for research purposes.
- **Academic Research**: Gather data for studies on digital marketing and communication.
- **Business Automation**: Automate the collection of relevant business insights.
### Installation and Usage
1. Search for "Accesscorp Blog 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 data is structured in JSON format with key fields such as `title`, `url`, `content`, `date`, and more. Each field provides specific information about the blog post, making it easy to analyze and integrate into various applications.
### 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
## `Urls` (type: `array`):
The 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
{
"Urls": [
"https://www.accesscorp.com/blog/the-science-of-pathology-storage-balancing-compliance-and-efficiency/",
"https://www.accesscorp.com/blog/from-forecast-to-recovery-an-information-governance-checklist-for-hurricane-season/"
],
"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/accesscrop-blog-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/accesscrop-blog-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/accesscrop-blog-info-parser-spider --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=getdataforme/accesscrop-blog-info-parser-spider",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Accesscrop Blog Info Parser Spider",
"description": "Accesscorp Blog Info Parser Spider is a web scraping tool that extracts detailed blog post data from Accesscorp's website, outputting structured JSON for easy integration and analysis....",
"version": "0.0",
"x-build-id": "gqwM5XnCr30sxZcn3"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/getdataforme~accesscrop-blog-info-parser-spider/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-getdataforme-accesscrop-blog-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": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/getdataforme~accesscrop-blog-info-parser-spider/runs": {
"post": {
"operationId": "runs-sync-getdataforme-accesscrop-blog-info-parser-spider",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/getdataforme~accesscrop-blog-info-parser-spider/run-sync": {
"post": {
"operationId": "run-sync-getdataforme-accesscrop-blog-info-parser-spider",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": {
"Urls": {
"title": "Urls",
"minItems": 1,
"type": "array",
"description": "The urls for the spider.",
"default": [
"https://www.accesscorp.com/blog/the-science-of-pathology-storage-balancing-compliance-and-efficiency/",
"https://www.accesscorp.com/blog/from-forecast-to-recovery-an-information-governance-checklist-for-hurricane-season/"
],
"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
}
}
}
}
}
}
}
}
}
}
```