# Amazon Seller & Brand Intel - MCP Server (`seibs.co/mcp-amazon-seller-intel`) Actor MCP server for amazon-seller-intel. AI-agent tools for Amazon seller records, product offers, brand audits, and modeled sales estimates. x402 (USDC on Base) and Skyfire agentic-payment ready. For brand owners, agencies, and e-commerce investors. - **URL**: https://apify.com/seibs.co/mcp-amazon-seller-intel.md - **Developed by:** [Seibs.co](https://apify.com/seibs.co) (community) - **Categories:** MCP servers, E-commerce, Business - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $5.00 / 1,000 mcp tool calls 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 ## Amazon Seller & Brand Intel - MCP Server > Model Context Protocol (MCP) server wrapper for [amazon-seller-intel](https://apify.com/seibs.co/amazon-seller-intel). Gives AI agents direct, pay-per-call access to Amazon seller profiles, Buy Box / offer maps, brand-to-seller audits, and BSR-based sales estimates - no API token required when called over x402 / Skyfire. ### What it is A thin MCP server that exposes the seller-intelligence engine as four typed tools an AI agent can call. Each tool runs the upstream `amazon-seller-intel` actor live and reshapes the result into small, deterministic JSON (no raw HTML). Built for brand-protection copilots, distributor-vetting agents, PL-research bots, and e-commerce intelligence workflows. Heads up: each tool call triggers a live upstream Amazon run over residential proxy (Amazon blocks datacenter traffic). Expect 1-3 minutes and some residential bandwidth per call. That bandwidth is the dominant cost - see Pricing. ### Tools | Tool | What it does | |---|---| | `get_seller_profile` | One seller's public profile by seller id or storefront URL: display name, published business name + address (when shown), rating percentages, ratings count, Just-Launched flag, storefront link. | | `get_asin_offers` | Product summary + the All-Offers-Display offer map for one ASIN (seller, price + shipping, condition, FBA/FBM/AMZ, Buy Box flag). The summary sentence names who holds the Buy Box. | | `estimate_asin_sales` | ONLY the BSR-based sales estimate for one ASIN: a monthly unit-sales band, a revenue band, a confidence, the methodology, and the caveats. A modeled estimate, not measured sales. | | `audit_brand` | The brand-protection roll-up: distinct sellers + estimated share, price dispersion, Buy-Box control %, total estimated monthly sales band over the sampled products. | All tools take an optional `marketplace` of `com` (default), `co.uk`, `de`, or `ca`. ### Run modes - `list_tools` - emit the tool catalog (free, no charge) including the agentic-payment descriptor. - `call_tool` - invoke one tool. Requires `tool` + `args`. - `batch` - invoke up to 10 `{tool, args}` calls in one run. ```json { "mode": "call_tool", "tool": "get_asin_offers", "args": { "asin": "B07ZPKBL9V", "marketplace": "com", "max_offers": 10 } } ```` ### Agentic payments (x402 + Skyfire) This server is **x402 (USDC on Base)** and **Skyfire** ready. When the operator enables Apify MCP monetization, an AI agent can pay per tool call with no pre-provisioned API token. The `list_tools` response includes a `payments` descriptor advertising the accepted rails and per-call price. Operators enable rails via environment variables: | Env | Purpose | |---|---| | `X402_ENABLED` | `1` to advertise x402 acceptance | | `X402_PAY_TO_ADDRESS` | receiving wallet (USDC on Base) | | `X402_PRICE_USD` | per-call price advertised (default 0.005) | | `SKYFIRE_ENABLED` | `1` to advertise Skyfire acceptance | | `SKYFIRE_SELLER_ID` | Skyfire seller identity | Calls through Apify always bill via standard PPE (`mcp_tool_call` $0.005 + upstream pass-through); x402/Skyfire are the token-less rails layered on top for direct agents. ### Pricing Flat **$0.005 per MCP tool call**, plus the upstream `amazon-seller-intel` PPE pass-through billed to the same run: | Upstream event | Price | |---|---| | `seller_record` | $0.012 | | `product_offer_record` | $0.005 (one per kept offer) | | `sales_estimate_enrichment` | $0.010 | | `brand_audit_record` | $0.015 | Each tool call runs a live Amazon fetch over residential proxy; that residential bandwidth is the dominant cost driver, not the PPE events. A run that returns nothing costs nothing. ### Responsible use / data scope The upstream actor reads only logged-out, public Amazon pages - no accounts, no cookies, no login, no paywall bypass. It returns business firmographics (a seller's published legal name and business address, ratings, offer and Buy-Box data) and never harvests personal contact data (emails, phones). Sales figures are modeled estimates from public Best Sellers Rank, not measured unit sales - treat the band, not the midpoint, as the answer, and never present an estimate as a measurement. You are responsible for lawful, ToS-compliant use of the outputs. ### Related Actors - [amazon-seller-intel](https://apify.com/seibs.co/amazon-seller-intel) - the non-MCP actor with full input controls, monitor mode, and CSV/HTML artifacts. - [mcp-hiring-signal-intel](https://apify.com/seibs.co/mcp-hiring-signal-intel) - the same MCP pattern for live hiring-signal intelligence. ### Found this useful? Leave a quick review: https://apify.com/seibs.co/mcp-amazon-seller-intel#reviews ### Changelog See [CHANGELOG.md](./CHANGELOG.md). # Actor input Schema ## `mode` (type: `string`): list\_tools = emit the MCP tool catalog (free). call\_tool = invoke one tool (requires 'tool' + 'args'). batch = invoke a list of {tool, args} calls. Hard cap of 10 calls per batch run. ## `tool` (type: `string`): Required when mode=call\_tool. One of: get\_seller\_profile, get\_asin\_offers, estimate\_asin\_sales, audit\_brand. ## `args` (type: `object`): Arguments for the selected tool. Example for get\_asin\_offers: {"asin": "B07ZPKBL9V", "marketplace": "com", "max\_offers": 10}. Example for audit\_brand: {"brand": "Anker", "max\_products": 12}. marketplace is one of com / co.uk / de / ca and defaults to com. ## `calls` (type: `array`): Required when mode=batch. Each entry is an object with 'tool' and 'args'. Example: \[{"tool": "get\_seller\_profile", "args": {"seller\_id": "A2R2RITDJNW1Q6"}}, {"tool": "audit\_brand", "args": {"brand": "Anker"}}]. Max 10 calls per run. Each call is a live 1-3 minute Amazon run. ## Actor input object example ```json { "mode": "list_tools", "tool": "get_asin_offers", "args": { "asin": "B07ZPKBL9V", "marketplace": "com", "max_offers": 10 }, "calls": [] } ``` # 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 = { "mode": "list_tools", "args": { "asin": "B07ZPKBL9V", "marketplace": "com", "max_offers": 10 } }; // Run the Actor and wait for it to finish const run = await client.actor("seibs.co/mcp-amazon-seller-intel").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 = { "mode": "list_tools", "args": { "asin": "B07ZPKBL9V", "marketplace": "com", "max_offers": 10, }, } # Run the Actor and wait for it to finish run = client.actor("seibs.co/mcp-amazon-seller-intel").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 '{ "mode": "list_tools", "args": { "asin": "B07ZPKBL9V", "marketplace": "com", "max_offers": 10 } }' | apify call seibs.co/mcp-amazon-seller-intel --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seibs.co/mcp-amazon-seller-intel", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Amazon Seller & Brand Intel - MCP Server", "description": "MCP server for amazon-seller-intel. AI-agent tools for Amazon seller records, product offers, brand audits, and modeled sales estimates. x402 (USDC on Base) and Skyfire agentic-payment ready. For brand owners, agencies, and e-commerce investors.", "version": "0.1", "x-build-id": "dPptpzPshGesfd6vs" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seibs.co~mcp-amazon-seller-intel/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seibs.co-mcp-amazon-seller-intel", "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/seibs.co~mcp-amazon-seller-intel/runs": { "post": { "operationId": "runs-sync-seibs.co-mcp-amazon-seller-intel", "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/seibs.co~mcp-amazon-seller-intel/run-sync": { "post": { "operationId": "run-sync-seibs.co-mcp-amazon-seller-intel", "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": [ "mode" ], "properties": { "mode": { "title": "Mode", "enum": [ "list_tools", "call_tool", "batch" ], "type": "string", "description": "list_tools = emit the MCP tool catalog (free). call_tool = invoke one tool (requires 'tool' + 'args'). batch = invoke a list of {tool, args} calls. Hard cap of 10 calls per batch run.", "default": "list_tools" }, "tool": { "title": "Tool name", "enum": [ "get_seller_profile", "get_asin_offers", "estimate_asin_sales", "audit_brand" ], "type": "string", "description": "Required when mode=call_tool. One of: get_seller_profile, get_asin_offers, estimate_asin_sales, audit_brand.", "default": "get_asin_offers" }, "args": { "title": "Tool arguments (JSON object)", "type": "object", "description": "Arguments for the selected tool. Example for get_asin_offers: {\"asin\": \"B07ZPKBL9V\", \"marketplace\": \"com\", \"max_offers\": 10}. Example for audit_brand: {\"brand\": \"Anker\", \"max_products\": 12}. marketplace is one of com / co.uk / de / ca and defaults to com." }, "calls": { "title": "Batch calls", "maxItems": 10, "type": "array", "description": "Required when mode=batch. Each entry is an object with 'tool' and 'args'. Example: [{\"tool\": \"get_seller_profile\", \"args\": {\"seller_id\": \"A2R2RITDJNW1Q6\"}}, {\"tool\": \"audit_brand\", \"args\": {\"brand\": \"Anker\"}}]. Max 10 calls per run. Each call is a live 1-3 minute Amazon run.", "default": [] } } }, "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 } } } } } } } } } } ```