# Nolato Categories Spider (`getdataforme/nolato-categories-spider`) Actor The Nolato Categories Spider efficiently scrapes category data from Nolato's website using customizable keywords, delivering structured JSON outputs with titles, descriptions, and URLs.... - **URL**: https://apify.com/getdataforme/nolato-categories-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 ### Description The Nolato Categories Spider efficiently scrapes category data from Nolato's website using customizable keywords, delivering structured JSON outputs with titles, descriptions, and URLs.... --- ## Nolato Categories Spider ### Introduction The Nolato Categories Spider is a powerful Apify Actor designed to scrape and extract category information from the Nolato website based on user-defined keywords. It efficiently navigates the site to gather relevant data, providing structured outputs for analysis and integration. This tool is ideal for businesses and researchers seeking automated data collection from Nolato's industry-focused pages. ### Features - **Keyword-Based Scraping**: Targets specific categories using customizable keywords for precise data extraction. - **Structured Data Output**: Delivers clean, JSON-formatted results with fields like title, description, URL, and metadata. - **High Reliability**: Built with robust error handling to ensure consistent performance across runs. - **Fast Execution**: Optimized for quick scraping, minimizing runtime while maximizing data yield. - **Apify Integration**: Seamlessly integrates with Apify's platform for easy deployment, monitoring, and result export. - **Scalable**: Handles multiple keywords and large datasets without compromising on quality. - **User-Friendly**: Simple input configuration with no coding required. ### Input Parameters | Parameter | Type | Required | Description | Example | |-----------|--------|----------|-------------|---------| | Keyword | string | No | The keyword to search for categories on the Nolato website. Defaults to a general term if not specified. | "medical solutions" | ### Example Usage #### Input Example ```json { "Keyword": "medical solutions" } ```` #### Output Example ```json [ { "keyword": "medical solutions", "title": "Medical Devices", "description": "Nolato partners with the healthcare and pharmaceutical industries to develop and manufacture complex product systems and components using cutting-edge polymer technology and advanced automation.", "url": "https://www.nolato.com/en/Industries/Medical-Solutions/Medical-Devices", "actor_id": "Skb4BtHeZgfGQID6r", "run_id": "zR9dbxxC5YOOmxiCM" }, { "keyword": "medical solutions", "title": "Industries", "description": "Our experience spans virtually every industry and a vast range of challenges, giving us a unique perspective that we bring to every client relationship. We help you view things differently, discover opportunities you may never have considered possible and achieve outstanding results.", "url": "https://www.nolato.com/en/Industries", "actor_id": "Skb4BtHeZgfGQID6r", "run_id": "zR9dbxxC5YOOmxiCM" }, { "keyword": "medical solutions", "title": "Medical Solutions", "description": "Nolato offer design, development, industrialization and production of advanced components, subsystems and pre-packaged products for medical technology, the pharmaceutical industry and diagnostics.", "url": "https://www.nolato.com/en/Industries/Medical-Solutions", "actor_id": "Skb4BtHeZgfGQID6r", "run_id": "zR9dbxxC5YOOmxiCM" } ] ``` ### Use Cases - **Market Research**: Analyze industry categories to identify trends and opportunities in sectors like medical solutions. - **Competitive Intelligence**: Gather data on Nolato's offerings to benchmark against competitors. - **Content Aggregation**: Collect descriptions and URLs for building knowledge bases or reports. - **Academic Research**: Extract structured information for studies on industrial technologies and partnerships. - **Business Automation**: Automate data feeds for dashboards or CRM systems focused on specific keywords. - **SEO Analysis**: Use category data to inform search engine optimization strategies for related websites. ### Installation and Usage 1. Search for "Nolato Categories Spider" 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 an array of JSON objects, each representing a scraped category. Key fields include: - `keyword`: The input keyword used for the search. - `title`: The category title. - `description`: A brief description of the category. - `url`: The direct link to the category page. - `actor_id`: Unique identifier for the Actor run. - `run_id`: Unique identifier for the specific run. This structure ensures easy parsing and integration into downstream applications. ### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: https://getdataforme.com/contact/ We're here to help you get the most out of this Actor! *** # Actor input Schema ## `Keyword` (type: `string`): The keyword for the spider. ## Actor input object example ```json { "Keyword": "medical solutions" } ``` # 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/nolato-categories-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/nolato-categories-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/nolato-categories-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/nolato-categories-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Nolato Categories Spider", "description": "The Nolato Categories Spider efficiently scrapes category data from Nolato's website using customizable keywords, delivering structured JSON outputs with titles, descriptions, and URLs....", "version": "0.0", "x-build-id": "9qOihAPxTgYp8bRac" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~nolato-categories-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-nolato-categories-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~nolato-categories-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-nolato-categories-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~nolato-categories-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-nolato-categories-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "Keyword": { "title": "Keyword", "type": "string", "description": "The keyword for the spider.", "default": "medical solutions" } } }, "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 } } } } } } } } } } ```