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Enter a domain on the left and run the test. Results stream in here.
Paste the page you want to check for a TL;DR or summary block.
The tool looks for summary cue phrases and weights them by how near the top of the page they appear.
See whether a summary block was found, where it sits, and add or move one to the top if it is missing.
A clearly labeled TL;DR, summary, or key-takeaways block hands AI engines a pre-digested version of your page that is easy to lift and cite. It also signals answer-first structure, which both AI systems and featured snippets reward. Pages that summarize their main points up top are more likely to be quoted accurately for the query.
It scans the page for summary cue phrases — 'TL;DR', 'TLDR', 'key takeaways', 'in summary', 'in short', 'quick answer', 'the bottom line' — in headings and early content, and weights matches by how near the top of the page they appear. A labeled block high on the page scores as a confident detection; a buried one is reported as weak.
Place it near the top — right after the H1 or the introduction, above the fold. The whole value of a TL;DR for AI and human scanners is immediacy: it should be one of the first things encountered. A summary stuck at the very bottom still helps a little but loses most of its answer-first advantage.
Two to four sentences, or a short bulleted list, that state the core answer and the most important takeaways in plain language. It should stand alone without requiring the rest of the article, mirror the page's main query, and avoid teasers like 'read on to find out'. Make it the passage you would want an AI to quote.
No. 'Key takeaways', 'Summary', 'In short', 'Quick answer' and similar labels work just as well and the detector recognizes them. The important thing is an explicit, scannable label that marks the block as a summary, plus placement near the top. Pick whichever wording fits your brand voice.
No — it helps both audiences. Scanners get the answer immediately, while readers who want detail simply continue past it into the full article. This is the same inverted-pyramid technique used in journalism, and it tends to reduce bounce while improving AI and snippet eligibility.
No. Detection is rule-based keyword and position analysis over the parsed page, run on our server with no LLM or external AI API. The page is analyzed only to detect and locate a summary block and is not stored or used for training.