# E-commerce Listing Optimizer (`magicfingers/listing-optimizer`) Actor Scrapes competitor product listings from Amazon, Shopify, and eBay. Uses an LLM (OpenAI or Anthropic) to analyze competitor strengths and generate optimized listing content for your products. - **URL**: https://apify.com/magicfingers/listing-optimizer.md - **Developed by:** [abdulrahman alrashid](https://apify.com/magicfingers) (community) - **Categories:** E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 undefined # Actor input Schema ## `competitorUrls` (type: `array`): Array of competitor product page URLs from Amazon, Shopify, or eBay. ## `yourProductDescription` (type: `string`): Describe your product in plain text. Include key details like brand, category, features, and target audience. ## `yourProductFeatures` (type: `array`): Key features of your product as individual items. ## `targetMarketplace` (type: `string`): The marketplace you are optimizing the listing for. ## `maxReviewsPerCompetitor` (type: `integer`): Maximum number of reviews to scrape per competitor listing. ## `llmProvider` (type: `string`): Choose which LLM provider to use for generating optimized content. Leave empty to get raw competitor data only. ## `llmApiKey` (type: `string`): Your API key for the chosen LLM provider. If not provided, the actor returns raw competitor data without LLM optimization. ## `llmModel` (type: `string`): Model to use. For OpenAI: gpt-4o-mini, gpt-4o, etc. For Anthropic: claude-sonnet-4-20250514, etc. ## `proxyConfiguration` (type: `object`): Proxy settings for scraping. Recommended for Amazon to avoid blocks. ## Actor input object example ```json { "competitorUrls": [ "https://www.amazon.com/dp/B0C8QJ5V56" ], "yourProductDescription": "Our product is a wireless Bluetooth headphone with active noise cancellation, 40-hour battery, Bluetooth 5.3, and a lightweight foldable design. Targeted at commuters and remote workers.", "yourProductFeatures": [ "Active Noise Cancellation", "40-hour battery life", "Bluetooth 5.3", "Foldable lightweight design", "Built-in microphone with ENC" ], "targetMarketplace": "Amazon", "maxReviewsPerCompetitor": 10, "llmProvider": "", "llmModel": "gpt-4o-mini", "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } ```` # 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 = { "competitorUrls": [ "https://www.amazon.com/dp/B0C8QJ5V56" ], "yourProductDescription": "Our product is a wireless Bluetooth headphone with active noise cancellation, 40-hour battery, Bluetooth 5.3, and a lightweight foldable design. Targeted at commuters and remote workers.", "yourProductFeatures": [ "Active Noise Cancellation", "40-hour battery life", "Bluetooth 5.3", "Foldable lightweight design", "Built-in microphone with ENC" ], "maxReviewsPerCompetitor": 10, "llmModel": "gpt-4o-mini", "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }; // Run the Actor and wait for it to finish const run = await client.actor("magicfingers/listing-optimizer").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 = { "competitorUrls": ["https://www.amazon.com/dp/B0C8QJ5V56"], "yourProductDescription": "Our product is a wireless Bluetooth headphone with active noise cancellation, 40-hour battery, Bluetooth 5.3, and a lightweight foldable design. Targeted at commuters and remote workers.", "yourProductFeatures": [ "Active Noise Cancellation", "40-hour battery life", "Bluetooth 5.3", "Foldable lightweight design", "Built-in microphone with ENC", ], "maxReviewsPerCompetitor": 10, "llmModel": "gpt-4o-mini", "proxyConfiguration": { "useApifyProxy": True, "apifyProxyGroups": ["RESIDENTIAL"], }, } # Run the Actor and wait for it to finish run = client.actor("magicfingers/listing-optimizer").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 '{ "competitorUrls": [ "https://www.amazon.com/dp/B0C8QJ5V56" ], "yourProductDescription": "Our product is a wireless Bluetooth headphone with active noise cancellation, 40-hour battery, Bluetooth 5.3, and a lightweight foldable design. Targeted at commuters and remote workers.", "yourProductFeatures": [ "Active Noise Cancellation", "40-hour battery life", "Bluetooth 5.3", "Foldable lightweight design", "Built-in microphone with ENC" ], "maxReviewsPerCompetitor": 10, "llmModel": "gpt-4o-mini", "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }' | apify call magicfingers/listing-optimizer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=magicfingers/listing-optimizer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "E-commerce Listing Optimizer", "description": "Scrapes competitor product listings from Amazon, Shopify, and eBay. Uses an LLM (OpenAI or Anthropic) to analyze competitor strengths and generate optimized listing content for your products.", "version": "1.0", "x-build-id": "qXnO11vQc7b5TZY3c" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/magicfingers~listing-optimizer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-magicfingers-listing-optimizer", "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/magicfingers~listing-optimizer/runs": { "post": { "operationId": "runs-sync-magicfingers-listing-optimizer", "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/magicfingers~listing-optimizer/run-sync": { "post": { "operationId": "run-sync-magicfingers-listing-optimizer", "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": [ "competitorUrls" ], "properties": { "competitorUrls": { "title": "Competitor Product URLs", "type": "array", "description": "Array of competitor product page URLs from Amazon, Shopify, or eBay.", "items": { "type": "string" } }, "yourProductDescription": { "title": "Your Product Description", "type": "string", "description": "Describe your product in plain text. Include key details like brand, category, features, and target audience." }, "yourProductFeatures": { "title": "Your Product Features", "type": "array", "description": "Key features of your product as individual items.", "items": { "type": "string" } }, "targetMarketplace": { "title": "Target Marketplace", "enum": [ "Amazon", "Shopify", "eBay", "Generic" ], "type": "string", "description": "The marketplace you are optimizing the listing for.", "default": "Amazon" }, "maxReviewsPerCompetitor": { "title": "Max Reviews per Competitor", "minimum": 0, "maximum": 500, "type": "integer", "description": "Maximum number of reviews to scrape per competitor listing.", "default": 50 }, "llmProvider": { "title": "LLM Provider", "enum": [ "OpenAI", "Anthropic", "" ], "type": "string", "description": "Choose which LLM provider to use for generating optimized content. Leave empty to get raw competitor data only.", "default": "" }, "llmApiKey": { "title": "LLM API Key", "type": "string", "description": "Your API key for the chosen LLM provider. If not provided, the actor returns raw competitor data without LLM optimization." }, "llmModel": { "title": "LLM Model", "type": "string", "description": "Model to use. For OpenAI: gpt-4o-mini, gpt-4o, etc. For Anthropic: claude-sonnet-4-20250514, etc.", "default": "gpt-4o-mini" }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Proxy settings for scraping. Recommended for Amazon to avoid blocks." } } }, "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 } } } } } } } } } } ```