# \[TEST] Platform Usage (`caprolok/test-actor-proxy`) Actor - **URL**: https://apify.com/caprolok/test-actor-proxy.md - **Developed by:** [Caprolok](https://apify.com/caprolok) (community) - **Categories:** AI, Agents, Automation - **Stats:** 1 total users, 1 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event 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 ### 🌐 Agoda Data Scraper The Agoda Data Scraper is designed to extract detailed hotel listings, including prices, ratings, reviews, and other key data points from Agoda. This scraper is ideal for travelers, market researchers, and anyone looking to analyze Agoda hotel listings based on location, check-in/check-out dates, and guest preferences. ### 🚀 Features - **Comprehensive Data Extraction::** Extracts detailed hotel information such as names, prices, reviews, amenities, and more. - **Customizable Search::** Allows you to search hotels based on location, check-in/out dates, and the number of guests (adults, children). - **Scalability::** Scrape maximum listings at once. - **Pagination Support::** Automatically handles pagination to scrape multiple pages of listings. ### 🔍 Scraped Data The scraper extracts a wide range of data points from Agoda listings, including: - Hotel Name: Name of the hotel. - Price: Price of the room per night. - Location: City/Area where the hotel is located. - Ratings and Reviews: Ratings, number of reviews, and detailed reviews of the hotel. - Images: URLs of images associated with the hotel. - Amenities: Facilities and amenities provided by the hotel. - Rooms: Information about available rooms, including room types, prices, and amenities. - Nearby Landmarks: Popular landmarks near the hotel. ### 📋 Input Parameters Field | Description | Example Input ---------------|-------------------------------------------------------------------|--------------- startUrls | Array of Agoda URLs to scrape hotel data from | ["https://www.agoda.com/search?city=8801&locale=en-us&checkIn=2024-08-14&checkOut=2024-08-15&rooms=1"] location | Specify the city or area for the Agoda search | "Hyderabad" checkin_date | Specify the check-in date (YYYY-MM-DD) | "2024-09-06" checkout_date | Specify the check-out date (YYYY-MM-DD) | "2024-09-07" adults | Number of adults | 2 children | Number of children | 0 rooms | Number of rooms required | 1 max_results | Maximum number of Agoda listings to scrape | 100 ### Sample Input ```json { "startUrls": [ "https://www.agoda.com/search?city=8801&locale=en-us&checkIn=2024-08-14&checkOut=2024-08-15&rooms=1" ], "location": "Hyderabad", "checkin_date": "2024-09-06", "checkout_date": "2024-09-07", "adults": 2, "children": 0, "rooms": 1, "max_results": 100 } ```` ### 📊 Output Format Field | Description \------------------------|---------------------------------------------------- hotel\_name | Name of the hotel price | Price of the room per night location | City/Area of the hotel ratings | Overall rating of the hotel review\_count | Total number of reviews for the hotel rating\_score | Rating score of the hotel rating\_description | Description of the rating (e.g., "Excellent", "Good") images | URLs of images related to the hotel amenities | Facilities and amenities provided by the hotel detailed\_page\_link | Link to the hotel's detailed page on Agoda rooms | Information about available rooms, including room type and price popular\_landmarks | Popular landmarks near the hotel property\_description | Short description of the hotel address | Address of the hotel discount\_availability | Discount percentage, if available reviews | Individual reviews from previous guests ### Example Output hotel\_name | price | location | ratings | review\_count | images\_link | amenities | address \------------------------|---------|--------------|---------|--------------|---------------------------------|--------------------|--------- Hyatt Regency | $150 | Hyderabad | 4.5 | 500 | https://example.com/image1.jpg | Free Wi-Fi, Parking | 123 Main St, Hyderabad Taj Deccan | $200 | Hyderabad | 4.7 | 800 | https://example.com/image2.jpg | Free Breakfast, Pool | 456 Hill St, Hyderabad ### ⚡ Performance - **Speed::** Scrapes up to 100 hotel listings in a few minutes, with support for up to 20,000 listings. - **Efficiency::** Extracts detailed hotel information, including room prices, reviews, and amenities. - **Accuracy::** Ensures all relevant details are captured, providing a comprehensive dataset. ### 🛠️ Customizable Scraping Options - **Location::** Input specific cities or areas for targeted searches. - **Check-in/Check-out Dates::** Specify the date range to find available hotels. - **Guest Count::** Customize the number of adults, children, and rooms required. - **Pagination::** Automatically handles pagination to scrape all available listings. ### 📊 Use Cases - **Travel Planning::** Compare and analyze hotel listings for your trip based on price, location, and amenities. - **Market Research::** Gather data for market research on hotel availability, pricing, and customer reviews. - **Competitor Analysis::** Analyze competitors' hotel offerings in specific regions. Investment Analysis: Evaluate hotel properties for potential investment opportunities. ### 🆘 Support If you encounter any issues or need assistance with the Agoda Data Scraper, feel free to reach out via the Issues tab on the actor's page. Our support team is here to assist with any concerns or questions you may have. # Actor input Schema ## `startUrls` (type: `array`): Insert a valid URL. ## `proxyConfiguration` (type: `object`): Select proxies to be used by your crawler. ## Actor input object example ```json { "startUrls": [ { "url": "" } ], "proxyConfiguration": { "useApifyProxy": true } } ``` # 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": "" } ], "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("caprolok/test-actor-proxy").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": "" }], "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("caprolok/test-actor-proxy").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": "" } ], "proxyConfiguration": { "useApifyProxy": true } }' | apify call caprolok/test-actor-proxy --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=caprolok/test-actor-proxy", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "[TEST] Platform Usage", "description": null, "version": "0.0", "x-build-id": "FHky3phI1tvJjriYX" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/caprolok~test-actor-proxy/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-caprolok-test-actor-proxy", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/caprolok~test-actor-proxy/runs": { "post": { "operationId": "runs-sync-caprolok-test-actor-proxy", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/caprolok~test-actor-proxy/run-sync": { "post": { "operationId": "run-sync-caprolok-test-actor-proxy", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "startUrls" ], "properties": { "startUrls": { "title": "proy test url", "type": "array", "description": "Insert a valid URL.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Select proxies to be used by your crawler." } } }, "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 } } } } } } } } } } ```