# The Artifact Machine - AI Access Audit (`artifact-machine/artifact-machine-ai-access-audit`) Actor A small artifact for checking whether AI search engines and AI assistants can access, cite, and understand a website. - **URL**: https://apify.com/artifact-machine/artifact-machine-ai-access-audit.md - **Developed by:** [The Artifact Machine](https://apify.com/artifact-machine) (community) - **Categories:** AI, SEO tools, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $0.50 / ai access audit report 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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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 ## The Artifact Machine AI Access Audit A small artifact for checking whether AI search engines and AI assistants can access, cite, and understand a website. Use this Actor before a site launch, content refresh, SEO review, or AI visibility campaign. It gives teams a fast crawlability and AI-access report for one target URL. Pulled from The Artifact Machine: generated, tested, and cleaned up by a human. ### What It Checks - `robots.txt` policy for AI search, AI user-agent, and training crawlers - `llms.txt` and `llms-full.txt` presence and basic structure - XML sitemap presence, `lastmod` coverage, and freshness - HTTPS, canonical, `noindex`, and `nosnippet` page access signals ### Best For - SEO and GEO audits - Agency client diagnostics - Site launch checks - AI answer visibility reviews - Monthly crawlability monitoring ### Input ```json { "url": "https://example.com", "includeRawContent": false } ```` #### Input Fields - `url`: homepage or canonical page URL to audit. - `includeRawContent`: optional debugging flag that includes truncated `robots.txt` and `llms.txt` snippets in the output. ### Output The Actor writes one dataset item and an `OUTPUT` key-value record with: - composite score and grade - issue counts by severity - check summaries for robots, llms, sitemap, and page access - actionable issues with impact and how-to-fix guidance Example output fields: - `score`: 0-100 composite AI access score - `grade`: A-F score band - `issueCounts`: issue totals by severity - `checks.robotsTxt`: AI crawler policy results - `checks.llmsTxt`: AI guidance file results - `checks.sitemap`: sitemap presence and freshness - `checks.pageAccess`: HTTPS, canonical, noindex, and snippet access - `issues`: prioritized fix list ### Limits - This Actor audits one submitted URL and the root-level policy files for that URL's origin. - It does not crawl an entire site. - It does not guarantee indexing or citation by any search engine or AI product. - Some sites block automated requests differently from regular browsers, so results should be reviewed before client delivery. ### Pricing Use pay-per-event pricing based on completed reports rather than per issue. The recommended first paid event is `audit-completed`. # Actor input Schema ## `url` (type: `string`): The homepage or canonical page URL to audit. ## `includeRawContent` (type: `boolean`): Include truncated robots.txt and llms.txt content in the output for debugging. ## Actor input object example ```json { "url": "https://example.com", "includeRawContent": false } ``` # Actor output Schema ## `results` (type: `string`): Default dataset items. Each item contains the target URL, score, grade, check summaries, issue counts, and actionable issues. ## `summary` (type: `string`): The same single-run summary stored as the OUTPUT key-value record. # 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 = { "url": "https://example.com" }; // Run the Actor and wait for it to finish const run = await client.actor("artifact-machine/artifact-machine-ai-access-audit").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 = { "url": "https://example.com" } # Run the Actor and wait for it to finish run = client.actor("artifact-machine/artifact-machine-ai-access-audit").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 '{ "url": "https://example.com" }' | apify call artifact-machine/artifact-machine-ai-access-audit --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=artifact-machine/artifact-machine-ai-access-audit", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "The Artifact Machine - AI Access Audit", "description": "A small artifact for checking whether AI search engines and AI assistants can access, cite, and understand a website.", "version": "0.1", "x-build-id": "VhTKHrM0ZDImLFdkn" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/artifact-machine~artifact-machine-ai-access-audit/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-artifact-machine-artifact-machine-ai-access-audit", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/artifact-machine~artifact-machine-ai-access-audit/runs": { "post": { "operationId": "runs-sync-artifact-machine-artifact-machine-ai-access-audit", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/artifact-machine~artifact-machine-ai-access-audit/run-sync": { "post": { "operationId": "run-sync-artifact-machine-artifact-machine-ai-access-audit", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "url" ], "properties": { "url": { "title": "Website URL", "type": "string", "description": "The homepage or canonical page URL to audit." }, "includeRawContent": { "title": "Include raw policy snippets", "type": "boolean", "description": "Include truncated robots.txt and llms.txt content in the output for debugging.", "default": 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 } } } } } } } } } } ```