Free Retrieval Readiness Analyzer
An answer engine retrieves a passage, not a page. This reads your article section by section and reports where a retriever would struggle to lift a clean, self-contained answer. Every check prints the study behind it, and four things other tools grade are deliberately left out.
How it works
Measured against published research, and honest about its limits.
Paste an article URL
We read the raw HTML, because that is what almost every AI crawler reads.
We split it into passages
A retriever returns a chunk, not a page, so each section is measured on its own.
Every check names its source
And four things other tools score are deliberately left out, with the studies that found them null.
A well-structured page still needs to be reachable. Confirm crawlers can get it with the AI crawler access test, and check the on-page basics with the article quality checker.
Frequently asked questions
What is content chunking, and why does it matter?
An answer engine does not read your page and summarise it. It retrieves a passage, usually a section, and works from that. So the unit that has to make sense on its own is the section, not the article. This tool measures each section as a retriever would meet it: alone, without the rest of the page around it.
Is the retrieval readiness analyzer free?
Yes. You can analyse up to 20 pages a day with no account, and up to 60 a day when you sign in with Google. No credit card.
What is answer depth?
How far into a section the first substantive sentence sits. A section that opens with two sentences of throat-clearing before saying anything concrete pushes the answer down the passage. Retrieval research measured a 15.6% average drop in effectiveness when the answer-bearing text appears late, declining steadily with depth.
Why does it not check schema markup or FAQ blocks?
Because the evidence says they do not help. A matched test of 1,885 pages found adding schema moved AI Overview citations by -4.6%, and Google states no special schema is needed. Q&A formatting measured -5.74% on citation absorption. Word count correlates at 0.04. We list all four on the results page with their sources rather than quietly scoring them.
Will fixing these things get me cited?
Nobody can promise that, and be wary of any tool that does. The position-bias evidence comes from embedding retrievers on benchmarks, not from the production systems behind ChatGPT or Google, whose chunking is not public. What this tells you is where a retriever would struggle to isolate a clean, self-contained passage. That is a real, fixable problem, and it is narrower than a citation guarantee.
How is this different from your article quality checker?
The Published Article Quality Checker looks at title, meta description, canonical, author, dates, links and alt text: whether a page is set up well for search. This looks at whether a passage survives retrieval. Different question, different evidence, and they pair well.