# AI Commerce Agent Readiness Auditor (`trovevault/ai-commerce-agent-readiness-auditor`) Actor Audits e-commerce sites for AI-agent shopping readiness with machine access, product schema, search, cart, policy, task friction, and fixes. - **URL**: https://apify.com/trovevault/ai-commerce-agent-readiness-auditor.md - **Developed by:** [Trove Vault](https://apify.com/trovevault) (community) - **Categories:** AI, Agents, MCP servers - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $4.25 / 1,000 tasks This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 ## AI Commerce Agent Readiness Auditor Audit an e-commerce site for the public signals that make it easier or harder for AI agents, agentic browsers, and LLM-assisted shoppers to discover products, understand availability, find policies, and move toward checkout. The actor loads each storefront in a real browser, checks machine-readable access, structured product data, search, product/category paths, cart affordances, policy discoverability, and task friction. It returns scores such as `agentReadinessScore`, `machineAccessScore`, `structuredDataScore`, `productDiscoverabilityScore`, `checkoutReadinessScore`, `policyDiscoverabilityScore`, `performanceScore`, `topIssues`, and `recommendedFixes`. This is an observable readiness auditor. It does not measure proprietary ranking inside ChatGPT, Gemini, Perplexity, Visa, New Gen, Kepler, or any private agent network. ### Why use this actor AI-driven shopping introduces a practical problem for e-commerce teams: a store can look fine to humans while still being awkward for software agents to parse, search, compare, or act on. Product pages may hide variants behind JavaScript-only UI, schema markup may be incomplete, policies may be hard to find, or search may not accept natural-language tasks cleanly. Use this actor when you need to answer: - Can a browser-based agent reach the storefront? - Are products, offers, prices, and availability visible in text or structured data? - Can a natural-language shopping task find a plausible product path? - Are shipping, returns, contact, support, and FAQ pages easy to discover? - Which fixes would improve readiness before a redesign or campaign? - Which technical fixes should the team prioritize first? ### What it produces For each store and each shopping task, the dataset includes: - `agentReadinessScore`: overall 0-100 readiness score. - `readinessLevel`: `excellent`, `good`, `average`, `poor`, or `blocked`. - `taskOutcome`: `completed`, `partial`, `blocked`, or `error`. - `failureReason`: the main observed reason a task did not complete cleanly. - Score components for machine access, structured data, product discovery, checkout readiness, policy discovery, and performance. - `visibleProductSignals`: product, search, cart, price, and schema signals. - `machineAccessSignals`: robots, sitemap, llms.txt, canonical, Open Graph, and metadata signals. - `topIssues`: the highest-impact issues found. - `recommendedFixes`: practical remediation steps. - `evidence`: compact counts and samples supporting the audit result. - `screenshotKey`: key-value store screenshot of the loaded storefront. The actor also writes `RUN_SUMMARY` with per-site summaries and the run average. ### Use cases **E-commerce operators** can run this before a launch, migration, template change, or merchandising push. **Commerce agencies** can audit prospective clients and show concrete technical work. **AEO and SEO teams** can add structured data, sitemap, policy, and product-discoverability checks to recurring audits. **Growth teams** can monitor whether product discovery and checkout readiness regress after site changes. ### Input The input is intentionally short: ```json { "startUrls": [ { "url": "https://demo.saleor.io/" } ], "shoppingTasks": [ "Find a product suitable as a gift", "Find shipping or delivery information" ], "proxyConfiguration": { "useApifyProxy": false }, "datasetId": "optional-existing-dataset-id", "runId": "optional-client-or-workflow-id" } ```` #### Input fields | Field | Description | | --- | --- | | `startUrls` | Storefront URLs to audit. Use public homepages or storefront entry points. | | `shoppingTasks` | Natural-language shopping jobs to test. Keep the list short for scheduled monitoring. | | `proxyConfiguration` | Optional Apify Proxy configuration for sites that block datacenter traffic or show regional flows. | | `datasetId` | Optional dataset to append results to, in addition to the default run dataset. | | `runId` | Optional workflow/client ID copied into rows for downstream joins. | ### Output example ```json { "inputUrl": "https://demo.saleor.io/", "finalUrl": "https://demo.saleor.io/default-channel", "domain": "demo.saleor.io", "auditStatus": "success", "shoppingTask": "Find a product suitable as a gift", "agentReadinessScore": 57, "readinessLevel": "average", "taskOutcome": "partial", "failureReason": "Product path was visible but price or cart signals were incomplete.", "stepsTaken": 2, "durationSeconds": 5.1, "interactionCount": 2, "machineAccessScore": 55, "structuredDataScore": 20, "productDiscoverabilityScore": 90, "checkoutReadinessScore": 75, "policyDiscoverabilityScore": 20, "performanceScore": 80, "visibleProductSignals": [ "product/category links visible", "storefront search visible" ], "machineAccessSignals": [ "robots.txt reachable", "sitemap.xml reachable", "canonical", "open graph" ], "topIssues": [ "Price, availability, cart, or checkout signals are weak before purchase." ], "recommendedFixes": [ "Make price, availability, variants, and add-to-cart actions visible in text and structured data." ], "evidence": { "homepageTitle": "ACME Storefront, powered by Saleor & Next.js | Saleor Store", "linkCount": 37, "schemaTypes": [], "taskEvidence": ["used storefront search", "price text visible"] }, "screenshotKey": "SCREENSHOT-demo-saleor-io-1780000000000", "auditedAt": "2026-06-17T12:00:00.000Z", "runId": "client-q3-audit" } ``` ### API usage Run the actor from the Apify API: ```bash curl -X POST "https://api.apify.com/v2/acts/YOUR_USERNAME~ai-commerce-agent-readiness-auditor/runs" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $APIFY_TOKEN" \ -d '{ "startUrls": [{ "url": "https://demo.saleor.io/" }], "shoppingTasks": [ "Find a product suitable as a gift", "Find shipping or delivery information" ], "proxyConfiguration": { "useApifyProxy": false } }' ``` Fetch dataset rows after the run: ```bash curl "https://api.apify.com/v2/datasets/DATASET_ID/items?clean=true" \ -H "Authorization: Bearer $APIFY_TOKEN" ``` ### How scoring works The score uses observable browser and HTTP signals: - Machine access: `robots.txt`, `sitemap.xml`, `llms.txt`, canonical tags, Open Graph, descriptions, and blocking. - Structured data: JSON-LD/schema.org `Product`, `Offer`, `Organization`, `WebSite`, and breadcrumb signals. - Product discoverability: product/category links, storefront search, visible product language, and homepage product paths. - Checkout readiness: price, availability, cart, checkout, and add-to-cart signals visible before purchase. - Policy discoverability: shipping, delivery, returns, refunds, support, FAQ, and contact links or text. - Performance: lightweight browser-observed load friction. - Task simulation: whether the actor can follow the supplied natural-language shopping task to a useful product or policy path. The scoring model is heuristic and designed for triage. Treat it as a practical readiness audit, not a legal, accessibility, SEO, or paid-agent certification. ### Limitations - The actor checks public observable signals only. - It does not complete purchases, submit payment details, log into accounts, or bypass access controls. - It does not call private New Gen, Kepler, Visa, ChatGPT, Gemini, or search-ranking APIs. - Heavily protected storefronts may require proxy settings or may still return blocked rows. - Some stores expose product data only after complex client-side interactions, which can lower scores even when a human can browse normally. - The actor does not guarantee that an LLM will cite, recommend, rank, or purchase from a store. - Results may vary by geography, site experiments, bot protection, personalization, and catalog changes. ### FAQ #### Is this the same as New Gen or Kepler Agent Score? No. It is inspired by the same market shift toward agentic commerce, but it is a TroveVault readiness auditor based on public browser and machine-readable signals. It does not use New Gen's private API or proprietary scoring. #### Can I schedule this? Yes. Use Apify schedules and optionally provide `datasetId` to append recurring results into one long-running dataset. #### Does it support MCP workflows? The actor produces structured dataset rows that can be consumed by MCP-enabled tools, dashboards, or downstream automations. It does not currently write directly into an MCP connector. #### Is scraping these sites legal? The actor visits public pages and reports observable technical signals. You are responsible for ensuring your usage complies with applicable law, website terms, and internal policies. The actor does not bypass logins, paywalls, or access controls. ### Related actors This pairs well with TroveVault product scrapers, SEO metadata scrapers, website compliance auditors, and competitive monitoring actors when you need a broader e-commerce readiness workflow. ### Changes #### 0.1.0 - Initial. - Browser-based storefront audit. - Machine access, structured data, product discovery, checkout readiness, policy discovery, performance, and task-friction scoring. - One row per store and shopping task. - `RUN_SUMMARY`, screenshot evidence, `datasetId`, and `runId` support. ### Support Open an issue with a sample storefront URL, the shopping task you expected to work, and the dataset row that looked wrong. # Actor input Schema ## `startUrls` (type: `array`): One or more public e-commerce site URLs to audit. Use homepages or storefront entry points for the clearest signal. The actor runs browser-based checks and returns a readiness score with evidence and recommended fixes. ## `shoppingTasks` (type: `array`): Natural-language shopping jobs to test. Use tasks that match how a shopper or AI agent would ask for products, policies, or purchase help. Keep the list short for routine monitoring. ## `proxyConfiguration` (type: `object`): Proxy settings for browser visits. Leave disabled for open stores. Enable Apify Proxy when a store blocks datacenter traffic, returns 403 or 429, or shows regional shopping flows only from specific locations. ## `datasetId` (type: `string`): ID of an existing Apify dataset to append audit rows to, in addition to the default run dataset. Use this for scheduled monitoring across multiple runs. ## `runId` (type: `string`): ID of an upstream client job, audit, or scheduled workflow. The actor copies this value into output rows so external systems can join results later. ## Actor input object example ```json { "startUrls": [ { "url": "https://demo.saleor.io/", "label": "Saleor demo storefront" } ], "shoppingTasks": [ "Find a product suitable as a gift", "Find shipping or delivery information" ], "proxyConfiguration": { "useApifyProxy": false } } ``` # Actor output Schema ## `datasetUrl` (type: `string`): No description ## `runSummaryUrl` (type: `string`): No description # 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 = { "startUrls": [ { "url": "https://demo.saleor.io/", "label": "Saleor demo storefront" } ], "shoppingTasks": [ "Find a product suitable as a gift", "Find shipping or delivery information" ] }; // Run the Actor and wait for it to finish const run = await client.actor("trovevault/ai-commerce-agent-readiness-auditor").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 = { "startUrls": [{ "url": "https://demo.saleor.io/", "label": "Saleor demo storefront", }], "shoppingTasks": [ "Find a product suitable as a gift", "Find shipping or delivery information", ], } # Run the Actor and wait for it to finish run = client.actor("trovevault/ai-commerce-agent-readiness-auditor").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 '{ "startUrls": [ { "url": "https://demo.saleor.io/", "label": "Saleor demo storefront" } ], "shoppingTasks": [ "Find a product suitable as a gift", "Find shipping or delivery information" ] }' | apify call trovevault/ai-commerce-agent-readiness-auditor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=trovevault/ai-commerce-agent-readiness-auditor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI Commerce Agent Readiness Auditor", "description": "Audits e-commerce sites for AI-agent shopping readiness with machine access, product schema, search, cart, policy, task friction, and fixes.", "version": "0.1", "x-build-id": "3UsmV9WeyEXd93yzK" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/trovevault~ai-commerce-agent-readiness-auditor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-trovevault-ai-commerce-agent-readiness-auditor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/trovevault~ai-commerce-agent-readiness-auditor/runs": { "post": { "operationId": "runs-sync-trovevault-ai-commerce-agent-readiness-auditor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/trovevault~ai-commerce-agent-readiness-auditor/run-sync": { "post": { "operationId": "run-sync-trovevault-ai-commerce-agent-readiness-auditor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "startUrls" ], "properties": { "startUrls": { "title": "Store URLs", "type": "array", "description": "One or more public e-commerce site URLs to audit. Use homepages or storefront entry points for the clearest signal. The actor runs browser-based checks and returns a readiness score with evidence and recommended fixes.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "shoppingTasks": { "title": "Shopping Tasks", "type": "array", "description": "Natural-language shopping jobs to test. Use tasks that match how a shopper or AI agent would ask for products, policies, or purchase help. Keep the list short for routine monitoring.", "items": { "type": "string" }, "default": [ "Find a product suitable as a gift", "Find shipping or delivery information" ] }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Proxy settings for browser visits. Leave disabled for open stores. Enable Apify Proxy when a store blocks datacenter traffic, returns 403 or 429, or shows regional shopping flows only from specific locations.", "default": { "useApifyProxy": false } }, "datasetId": { "title": "Dataset ID (optional)", "type": "string", "description": "ID of an existing Apify dataset to append audit rows to, in addition to the default run dataset. Use this for scheduled monitoring across multiple runs." }, "runId": { "title": "Run ID (optional)", "type": "string", "description": "ID of an upstream client job, audit, or scheduled workflow. The actor copies this value into output rows so external systems can join results later." } } }, "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 } } } } } } } } } } ```