# Google Shopping Scraper (Serper) (`ethereal_wool/google-shopping-serper-scraper`) Actor Scrape Google Shopping product results by search query — title, price, seller, rating and image — as structured JSON. - **URL**: https://apify.com/ethereal\_wool/google-shopping-serper-scraper.md - **Developed by:** [Jackie Chen](https://apify.com/ethereal_wool) (community) - **Categories:** E-commerce, SEO tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $4.00 / 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## Google Shopping Scraper (Serper) Scrape Google Shopping for any query and export structured product results. Each query returns products with their title, price, seller, rating, review count, image and Google product ID — via the Serper.dev Shopping API. > **Unofficial / independent tool.** This Actor is not affiliated with, authorized, sponsored, or endorsed by Google. It retrieves publicly available data through a third-party API. You are responsible for using the output in compliance with Google's terms and all applicable laws. ### What this Actor does This Actor focuses on one job: scrape google shopping product results by search query on **google.com**. Each query returns the products Google Shopping surfaces across merchants. - Returns Google Shopping products for each query. - Includes price and the selling merchant. - Reports rating and number of ratings. - Provides the product image, URL and Google product ID. ### Input | Field | Type | Description | | --- | --- | --- | | `searchQueries` | array | Google Shopping search queries (e.g. 'air fryer', 'mechanical keyboard'). One request per query. | | `maxItems` | integer | Maximum records to return (caps your spend). | | `proxyConfiguration` | object | Optional Apify Proxy settings. | #### Example input ```json { "maxItems": 5 } ```` ### Output The Actor returns one dataset item per shopping result. Each item is a flat, analysis-ready JSON record. Example of a real returned item: ```json { "title": "COSORI TurboBlaze Air Fryer", "price": "$119.99", "seller": "Cosori", "rating": 4, "ratingCount": 13, "imageUrl": "https://encrypted-tbn3.gstatic.com/shopping?q=tbn:ANd9GcR73H5YZFZtk2rpSGnvewjXMgSsoRMvAYeCmKVTjiu6Bu5UtVfyTP1crXEePfmKNONs7BcJQ0KwZ8GqbSatnZcm-ESA2LnUzD0Cgz8fMlyVULnY-T0PibPspQ", "productId": "15949526360554442507", "link": "https://www.google.com/search?ibp=oshop&q=air+fryer&prds=localAnnotatedOfferId:1,catalogid:15949526360554442507,pvo:2,pvt:hg,rds:PC_6027158597472037202%7CPROD_PC_6027158597472037202&gl=us&udm=28&pvorigin=2", "position": 1, "id": "15949526360554442507", "url": "https://www.google.com/search?ibp=oshop&q=air+fryer&prds=localAnnotatedOfferId:1,catalogid:15949526360554442507,pvo:2,pvt:hg,rds:PC_6027158597472037202%7CPROD_PC_6027158597472037202&gl=us&udm=28&pvorigin=2", "source": "google-shopping" } ``` #### Output fields | Field | Description | | --- | --- | | `title` | Product title | | `price` | Price (as shown) | | `seller` | Seller / merchant | | `rating` | Average rating | | `ratingCount` | Number of ratings | | `imageUrl` | Product image URL | | `productId` | Google product ID | | `link` | Product URL | | `position` | Result position | | `url` | Canonical link to the item on the source site | | `id` | Stable identifier for the item (when available) | | `source` | Which list / query the item came from | ### How it works - **Direct API, no browser.** Data is fetched over HTTP — no headless browser, no login, no cookies to manage. - **Honest failure.** Transient upstream blocks (rate limits, edge protection) are retried with exponential backoff. If the source stays unavailable, the run fails loudly instead of returning a misleading empty dataset. - **De-duplicated.** Items are de-duplicated by their identifier within a run. - **Pay per result.** Each delivered row charges one `result` event ($0.004); `maxItems` is a hard cap on both volume and spend. ### Use cases - Monitor competitor prices across merchants. - Track a product's sellers and rating over time. - Build a price-comparison or deal-finding feed. - Research product demand and market pricing. ### Integration Run it from the Apify Console, on a schedule, or call it programmatically via the Apify API, the JavaScript / Python clients, or MCP. Output can be exported as JSON, CSV, or Excel, or pushed to your own storage. ### FAQ **Do I need a Google account, cookies, or to log in?** No. The Actor only reads publicly available data. **How am I billed?** $0.004 per returned item; `maxItems` caps the total. **Can I schedule it or call it from my own code?** Yes — use Apify Schedules, the REST API, the official clients, or MCP. **Is this an official Google product?** No. It is an independent tool and is not affiliated with Google. # Actor input Schema ## `searchQueries` (type: `array`): Google Shopping search queries (e.g. 'air fryer', 'mechanical keyboard'). One request per query. ## `maxItems` (type: `integer`): Maximum number of records to return. Caps your spend. ## `proxyConfiguration` (type: `object`): Optional. Route API calls through Apify Proxy to vary the source IP. ## Actor input object example ```json { "searchQueries": [ "air fryer", "mechanical keyboard" ], "maxItems": 100, "proxyConfiguration": { "useApifyProxy": false } } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "searchQueries": [ "air fryer", "mechanical keyboard" ], "proxyConfiguration": { "useApifyProxy": false } }; // Run the Actor and wait for it to finish const run = await client.actor("ethereal_wool/google-shopping-serper-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "searchQueries": [ "air fryer", "mechanical keyboard", ], "proxyConfiguration": { "useApifyProxy": False }, } # Run the Actor and wait for it to finish run = client.actor("ethereal_wool/google-shopping-serper-scraper").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 '{ "searchQueries": [ "air fryer", "mechanical keyboard" ], "proxyConfiguration": { "useApifyProxy": false } }' | apify call ethereal_wool/google-shopping-serper-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ethereal_wool/google-shopping-serper-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Shopping Scraper (Serper)", "description": "Scrape Google Shopping product results by search query — title, price, seller, rating and image — as structured JSON.", "version": "0.1", "x-build-id": "IWVub5CQ7qlpk5kUT" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ethereal_wool~google-shopping-serper-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ethereal_wool-google-shopping-serper-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/ethereal_wool~google-shopping-serper-scraper/runs": { "post": { "operationId": "runs-sync-ethereal_wool-google-shopping-serper-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/ethereal_wool~google-shopping-serper-scraper/run-sync": { "post": { "operationId": "run-sync-ethereal_wool-google-shopping-serper-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "searchQueries": { "title": "Shopping search queries", "type": "array", "description": "Google Shopping search queries (e.g. 'air fryer', 'mechanical keyboard'). One request per query.", "items": { "type": "string" } }, "maxItems": { "title": "Max items", "minimum": 1, "maximum": 100000, "type": "integer", "description": "Maximum number of records to return. Caps your spend.", "default": 100 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Optional. Route API calls through Apify Proxy to vary the source IP.", "default": { "useApifyProxy": false } } } }, "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 } } } } } } } } } } ```