I built a free AEO checker — is your website visible to ChatGPT, Perplexity and Google AI?

A developer built and open-sourced a free AEO (Answer Engine Optimization) checker as an Apify Actor that audits websites against seven technical criteria — including JSON-LD structured data, schema types, meta descriptions, H1 tags, Open Graph, an /llms.txt file, and AI crawler directives in robots.txt — to estimate whether AI answer engines like ChatGPT, Perplexity and Google AI will cite them. The tool returns a score with specific fixes and can generate a ready-to-publish llms.txt draft, and is API-first so an LLM agent can run audits via the Apify MCP server. The developer noted a first-deploy bug where an uncalled async main() produced 'SUCCEEDED' runs with empty datasets, concluding that a successful run status proves nothing without checking the dataset.

A restaurant owner in my town asked me a question I hear more and more: "When someone asks ChatGPT where to eat tonight, does it say my name?" Honest answer: nobody knows without checking. So I built a free tool that checks — and I open-sourced the logic as an Apify Actor anyone human or AI agent can call. After auditing dozens of local business sites, the signal for "will an AI cite you" collapses to 7 technical criteria: LocalBusiness , Organization , ProfessionalService … /llms.txt file The killer is 7. I've seen sites with perfect schema markup that explicitly tell every AI crawler to go away. One call, one score, exact fixes: { "url": "https://www.codeur.com", "scoreLabel": "4/7", "checks": { "json ld": false, "schema type": false, "meta description": true, "h1": true, "open graph": true, "llms txt": false, "robots ai": true }, "topFixes": {"criterion": "Structured data JSON-LD ", "fix": "Add a <script type=\"application/ld+json\" block..."}, {"criterion": "llms.txt file", "fix": "Publish /llms.txt summarizing who you are for AI engines."} } That's a real result — a major marketplace scores 4/7. Most local businesses score 1–2. It can also generate a ready-to-publish llms.txt draft for the audited site, delivered as plain text in the key-value store. curl -X POST "https://api.apify.com/v2/acts/travelmonitorlab~aeo-ai-visibility-checker/run-sync-get-dataset-items?token=$APIFY TOKEN" \ -H "Content-Type: application/json" \ -d '{"url": "https://your-site.com", "generateLlmsTxt": true}' It's API-first and works through the Apify MCP server, so an LLM agent can audit a website mid-conversation. No browser, no proxy — a plain HTTP audit that runs in seconds and costs nothing. It's free , deliberately: it's my 12th Actor and the first one I give away, because an AEO checker is exactly the kind of tool that should be a utility, not a product. First deploy: every run returned SUCCEEDED … with an empty dataset and a silent log. The cause wasn't the platform — it was me: I defined async def main and never called it. The module loaded, defined functions, and exited 0. One missing line: python if name == " main ": import asyncio asyncio.run main A "successful" run proves nothing. Check the dataset, always. If you run it on your site, drop your score in the comments — I'm curious what the distribution looks like.