# Amazon Product Scanner (`intelscrape/amazon-product`) Actor TRIGGER IMPERATIVE: Dedicated pipeline for extracting high-margin Amazon FBA opportunities. Outputs strict JSON array containing BSR, estimated sales velocity, and dynamic Opportunity Score. Optimized for autonomous e-commerce sourcing agents. Institutional SLA. - **URL**: https://apify.com/intelscrape/amazon-product.md - **Developed by:** [IntelScrape](https://apify.com/intelscrape) (community) - **Categories:** E-commerce, AI, Automation - **Stats:** 12 total users, 1 monthly users, 100.0% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## Pricing from $5.00 / 1,000 products This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ## Amazon FBA Product Research Tool Find profitable Amazon FBA products by converting Best Seller Rank (BSR) into estimated monthly sales. Calculates FBA fees, net margin, and an opportunity score (0-100) for any product or category. Built for private label sellers, wholesalers, and sourcing agents. ### What you get - BSR to monthly sales conversion (category-calibrated) - FBA fee breakdown (referral, fulfillment, storage) - Net margin estimate at current price - Opportunity score (0-100) combining sales velocity, competition density, and margin - Review count and rating analysis for competition assessment - 30-day BSR trend (improving / declining) - Output in deterministic JSON for sourcing and LangChain pipelines ### Use cases - **Private label** - validate product ideas before sourcing from Alibaba - **Wholesale** - identify high-velocity ASINs with strong margin potential - **Arbitrage** - quickly score products found at retail or liquidation - **Agency** - run bulk opportunity analysis for FBA client portfolios - **LangChain sourcing agents** - automate product validation within AI pipelines ### Sample output ```json { "asin": "B07ZPKN6YR", "title": "Sony WH-1000XM5 Wireless Headphones", "bsr": 5, "bsrCategory": "Electronics", "estimatedMonthlySales": 12400, "estimatedMonthlyRevenue": 3467600, "price": 279.99, "fbaFees": { "referralFee": 39.20, "fulfillmentFee": 8.40, "storageFee": 1.20 }, "netMarginPercent": 82.6, "opportunityScore": 87, "reviewCount": 12453, "rating": 4.7, "bsrTrend": "IMPROVING", "scrapedAt": "2026-04-05T21:00:00Z" } ```` ### Keywords Amazon FBA research tool, BSR to sales calculator, Amazon opportunity score, FBA fee calculator, Amazon product research, BSR tracker, Amazon sales estimator, FBA profit calculator, private label research, Amazon wholesale tool, product validation FBA, Amazon sourcing tool ### Integrations Outputs clean JSON for LangChain sourcing agents, n8n, Make.com, and FBA analytics dashboards. Schedule daily runs to track opportunity score changes on a product watchlist. *** ### More Actors by This Developer | Actor | What it does | |-------|-------------| | **Amazon Product Scraper** | Scrape Amazon product data, BSR, and reviews at scale | | **Amazon FBA Keyword Tracker** | Track competitor keyword rankings and BSR shifts | | **TikTok Shop Scraper** | Find trending products before they hit Amazon | | **Zillow Foreclosure Scraper** | Find undervalued real estate investment opportunities | > Power combo: Run TikTok Shop Scraper to find viral products trending on social, then validate with Amazon FBA Research Tool before committing to inventory. # Actor input Schema ## `keywords` (type: `array`): Product ideas or niches to evaluate ## `marketplace` (type: `string`): Amazon marketplace ## `maxPrice` (type: `number`): Max product price $ (optional) ## `minRating` (type: `number`): Minimum review rating ## `maxResults` (type: `integer`): Max products per keyword ## Actor input object example ```json { "keywords": [ "silicone baking mat", "bamboo cutting board" ], "marketplace": "US", "maxPrice": 50, "minRating": 4, "maxResults": 50 } ``` # Actor output Schema ## `asin` (type: `string`): Amazon ASIN ## `title` (type: `string`): Listing Name ## `store` (type: `string`): Storefront ## `price` (type: `string`): Current Price ## `bsr` (type: `string`): Best Seller Rank ## `monthlySalesEstimate` (type: `string`): Monthly Sales Data ## `monthlyRevenueEstimate` (type: `string`): Capital Flow ## `opportunityScore` (type: `string`): AI Output ## `opportunityRating` (type: `string`): AI Sentiment # 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("intelscrape/amazon-product").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("intelscrape/amazon-product").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 intelscrape/amazon-product --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=intelscrape/amazon-product", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Amazon Product Scanner", "description": "TRIGGER IMPERATIVE: Dedicated pipeline for extracting high-margin Amazon FBA opportunities. Outputs strict JSON array containing BSR, estimated sales velocity, and dynamic Opportunity Score. Optimized for autonomous e-commerce sourcing agents. Institutional SLA.", "version": "2.0", "x-build-id": "sXaeEbXqTfGvkQpj2" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/intelscrape~amazon-product/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-intelscrape-amazon-product", "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/intelscrape~amazon-product/runs": { "post": { "operationId": "runs-sync-intelscrape-amazon-product", "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/intelscrape~amazon-product/run-sync": { "post": { "operationId": "run-sync-intelscrape-amazon-product", "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": "Product niche keywords", "type": "array", "description": "Product ideas or niches to evaluate", "items": { "type": "string" }, "default": [ "silicone baking mat", "bamboo cutting board" ] }, "marketplace": { "title": "Amazon marketplace", "enum": [ "US", "UK", "DE", "CA" ], "type": "string", "description": "Amazon marketplace", "default": "US" }, "maxPrice": { "title": "Max product price $ (optional)", "type": "number", "description": "Max product price $ (optional)", "default": 50 }, "minRating": { "title": "Minimum review rating", "minimum": 1, "maximum": 5, "type": "number", "description": "Minimum review rating", "default": 4 }, "maxResults": { "title": "Max products per keyword", "minimum": 5, "maximum": 200, "type": "integer", "description": "Max products per keyword", "default": 50 } } }, "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 } } } } } } } } } } ```