# Netdocuments Documents Info Parser Spider (`getdataforme/netdocuments-documents-info-parser-spider`) Actor The Netdocuments Documents Info Parser Spider is a web scraping tool that extracts detailed metadata from NetDocuments blog posts, including titles, publication dates, author details, and social media links.... - **URL**: https://apify.com/getdataforme/netdocuments-documents-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 --- ## README.md ### Netdocuments Documents Info Parser Spider #### Introduction The **Netdocuments Documents Info Parser Spider** is a powerful web scraping tool designed to extract detailed information from NetDocuments blog posts. It efficiently gathers data such as titles, publication dates, author details, and social media links, providing valuable insights for various applications. #### Features - **Comprehensive Data Extraction**: Captures essential metadata including title, date published, writer name, and more. - **High-Quality Output**: Ensures reliable and accurate data collection from NetDocuments blogs. - **Flexible Configuration**: Allows customization of URLs and item limits to suit specific needs. - **Efficient Performance**: Optimized for speed and resource management during scraping operations. - **User-Friendly Interface**: Easy setup with clear input parameters and straightforward execution. #### Input Parameters Table | Parameter | Type | Required | Description | Example | |--------------|----------|----------|-----------------------------------------------------------------------------|----------------------------------------------| | BlogUrls | array | Yes | The blog URLs for the spider. | `["https://www.netdocuments.com/blog/example"]` | | item_limit | integer | No | Maximum items to scrape per actor run. Set to 0 for no limit. | `10` | #### Example Usage ##### Input JSON ```json { "BlogUrls": [ "https://www.netdocuments.com/blog/true-ai-search-vs-ai-assisted-querying/" ], "item_limit": 10 } ```` ##### Output JSON ```json [ { "category": "BLOG", "title": "True AI Search vs. AI-Assisted Querying", "date_published": "January 21, 2026", "writer_image": "https://www.netdocuments.com/wp-content/uploads/2026/01/jared-beckstead.webp", "writer_name": "Jared Beckstead", "designation": "Senior Product Marketing Manager", "document_details": "", "topics": [ "AI", "AI search" ], "social_media_links": { "linkedin": "https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Fwww.netdocuments.com%2Fblog%2Ftrue-ai-search-vs-ai-assisted-querying%2F&title=True%20AI%20Search%20vs.%20AI-Assisted%20Querying", "facebook": "https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Fwww.netdocuments.com%2Fblog%2Ftrue-ai-search-vs-ai-assisted-querying%2F&title=True%20AI%20Search%20vs.%20AI-Assisted%20Querying", "x": "https://x.com/share?url=https%3A%2F%2Fwww.netdocuments.com%2Fblog%2Ftrue-ai-search-vs-ai-assisted-querying%2F&text=True%20AI%20Search%20vs.%20AI-Assisted%20Querying", "mail": "mailto:?subject=True%20AI%20Search%20vs.%20AI-Assisted%20Querying&body=True%20AI%20Search%20vs.%20AI-Assisted%20Querying%20\u2014%20https%3A%2F%2Fwww.netdocuments.com%2Fblog%2Ftrue-ai-search-vs-ai-assisted-querying%2F" }, "actor_id": "Wzf2SxRyL35GTEm4w", "run_id": "vr3kkHCL7yf3xqyvu" } ] ``` #### Use Cases - **Market Research and Analysis**: Extract insights from industry blogs to understand market trends. - **Competitive Intelligence**: Monitor competitors' blog content for strategic planning. - **Price Monitoring**: Track pricing strategies discussed in blog posts. - **Content Aggregation**: Compile relevant articles for newsletters or reports. - **Academic Research**: Gather data for studies on digital marketing and AI technologies. - **Business Automation**: Automate the collection of blog metrics for business analysis. #### Installation and Usage 1. Search for "Netdocuments Documents 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 fields such as `category`, `title`, `date_published`, `writer_image`, `writer_name`, `designation`, `document_details`, `topics`, and `social_media_links`. Each object represents a blog post with its associated metadata. #### Support Section ### 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 ## `BlogUrls` (type: `array`): The blog 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 { "BlogUrls": [ "https://www.netdocuments.com/blog/true-ai-search-vs-ai-assisted-querying/" ], "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/netdocuments-documents-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/netdocuments-documents-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/netdocuments-documents-info-parser-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/netdocuments-documents-info-parser-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Netdocuments Documents Info Parser Spider", "description": "The Netdocuments Documents Info Parser Spider is a web scraping tool that extracts detailed metadata from NetDocuments blog posts, including titles, publication dates, author details, and social media links....", "version": "0.0", "x-build-id": "PUAniUpeWxuEk5koN" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~netdocuments-documents-info-parser-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-netdocuments-documents-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~netdocuments-documents-info-parser-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-netdocuments-documents-info-parser-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~netdocuments-documents-info-parser-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-netdocuments-documents-info-parser-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "BlogUrls": { "title": "Blog Urls", "minItems": 1, "type": "array", "description": "The blog urls for the spider.", "default": [ "https://www.netdocuments.com/blog/true-ai-search-vs-ai-assisted-querying/" ], "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 } } } } } } } } } } ```