# Annoyme Product Discovery Ec Spider (`getdataforme/annoyme-product-discovery-ec-spider`) Actor The Annoyme Product Discovery Ec Spider efficiently extracts detailed product information from Anonyme, using keyword-based searches to gather insights for market research, competitive analysis, and price monitoring.... - **URL**: https://apify.com/getdataforme/annoyme-product-discovery-ec-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 --- ## Annoyme Product Discovery Ec Spider ### Introduction The Annoyme Product Discovery Ec Spider is a powerful web scraping tool designed to extract product data from the Anonyme website. It allows users to gather detailed information about products based on specified keywords, providing valuable insights for market research and competitive analysis. ### Features - **Keyword-Based Scraping**: Customize searches with specific keywords to target relevant products. - **High Data Quality**: Ensures accurate and reliable extraction of product details. - **Flexible Item Limits**: Set a maximum number of items to scrape per run or opt for unlimited scraping. - **Comprehensive Output**: Captures essential product attributes such as title, URL, image, price, and availability. - **Efficient Performance**: Optimized for speed and efficiency in data extraction. ### Input Parameters | Parameter | Type | Required | Description | Example | |-------------|---------|----------|-----------------------------------------------------------------------------|--------------------------| | Keywords | array | No | The keywords for the spider. | `["suit"]` | | item_limit | integer | No | Maximum items to scrape per actor run. Set to 0 for no limit. | `10` | ### Example Usage #### Input JSON ```json { "Keywords": ["suit"], "item_limit": 10 } ```` #### Output JSON ```json [ { "keyword": "suit", "title": "Suit Ceremony Blazer", "url": "https://anonyme.com/products/suit-ceremony-jacket-butter?variant=56752024551768", "image": "https://anonyme.com/cdn/shop/files/blazer_be0dc089-576e-418e-a172-2b80c8a5c185.png?v=1772027315&width=500", "price": "\u20ac119,00", "compare_at_price": null, "sold_out": true, "page": 1, "actor_id": "RJ6CNT9qoPbBwmFRh", "run_id": "nG9FIoXPoMyOUppn1" }, { "keyword": "suit", "title": "Suit Cool Gilet", "url": "https://anonyme.com/products/suit-cool-gilet-blue?variant=56752016851288", "image": "https://anonyme.com/cdn/shop/files/GILET_BLUE.png?v=1772014583&width=500", "price": "\u20ac89,00", "compare_at_price": null, "sold_out": true, "page": 1, "actor_id": "RJ6CNT9qoPbBwmFRh", "run_id": "nG9FIoXPoMyOUppn1" }, { "keyword": "suit", "title": "Suit Ceremony Blazer", "url": "https://anonyme.com/products/suit-ceremony-jacket-black?variant=56752024387928", "image": "https://anonyme.com/cdn/shop/files/blazer_nero_c14fd2b5-9ca2-4d74-99e7-500395d6c285.png?v=1772027181&width=500", "price": "\u20ac119,00", "compare_at_price": null, "sold_out": true, "page": 1, "actor_id": "RJ6CNT9qoPbBwmFRh", "run_id": "nG9FIoXPoMyOUppn1" } ] ``` ### Use Cases - **Market Research and Analysis**: Gather data to understand market trends and consumer preferences. - **Competitive Intelligence**: Monitor competitors' product offerings and pricing strategies. - **Price Monitoring**: Track price changes over time for strategic decision-making. - **Content Aggregation**: Compile product information for content creation or marketing purposes. - **Academic Research**: Collect data for studies on e-commerce patterns and behaviors. - **Business Automation**: Integrate with business processes to automate data-driven tasks. ### Installation and Usage 1. Search for "Annoyme Product Discovery Ec 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 the following fields: - `keyword`: The search keyword used for scraping. - `title`: The product title. - `url`: The URL of the product page. - `image`: The URL of the product image. - `price`: The current price of the product. - `compare_at_price`: The original price, if available. - `sold_out`: Boolean indicating if the product is sold out. - `page`: The page number from which the product was scraped. - `actor_id`: Unique identifier for the actor run. - `run_id`: Unique identifier for the specific run. ### Error Handling The spider includes error handling to manage common issues such as network errors, missing data fields, and rate limiting. If an error occurs, it will be logged in the Apify Console for review and troubleshooting. ### Rate Limiting and Best Practices To avoid being blocked by the target website, adhere to the following best practices: - Respect the site's `robots.txt` file. - Implement delays between requests if necessary. - Use a pool of rotating IP addresses or proxies if available. ### Limitations and Considerations - The spider is designed specifically for Anonyme.com; it may not work with other websites without modifications. - Ensure compliance with legal and ethical guidelines when scraping data. - Be aware of potential changes in the website's structure that could affect data extraction. # Actor input Schema ## `Keywords` (type: `array`): The keywords 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 { "Keywords": [ "suit" ], "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/annoyme-product-discovery-ec-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/annoyme-product-discovery-ec-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/annoyme-product-discovery-ec-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/annoyme-product-discovery-ec-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Annoyme Product Discovery Ec Spider", "description": "The Annoyme Product Discovery Ec Spider efficiently extracts detailed product information from Anonyme, using keyword-based searches to gather insights for market research, competitive analysis, and price monitoring....", "version": "0.0", "x-build-id": "S3YbMGb5R3wEGRpUc" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~annoyme-product-discovery-ec-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-annoyme-product-discovery-ec-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~annoyme-product-discovery-ec-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-annoyme-product-discovery-ec-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~annoyme-product-discovery-ec-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-annoyme-product-discovery-ec-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "Keywords": { "title": "Keywords", "type": "array", "description": "The keywords for the spider.", "default": [ "suit" ], "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 } } } } } } } } } } ```