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For whoever maintains the thing people ask about

Nobody is reading your documentation.

They are asking an agent about your product instead. It answers from a snapshot taken before your last two releases, in the same confident voice it uses for things it knows — and the result reaches you as an issue with a stack trace against a function you deleted.

Writing better docs does not fix this

At the moment it answers, the agent is not reading anything. It is recalling — and what it recalls is whatever your site looked like when the model was trained. Rewriting the page changes the next training run, months from now, if you are large enough to be worth learning properly.

An agent reads your pages only when something hands them over mid-task — a fetch it decided to make, or a tool it was given. That is the whole of the opening, and it is narrow enough to be worth aiming at deliberately.

What a brain does about it

the tool the agent calls
1

It is read at answer time

Your documentation becomes searchable notes the agent queries mid-task over MCP. It takes the three it needs, not the whole site, so the answer costs a few hundred tokens rather than a context window.

2

It sits an exam it did not write

Your stated goal becomes control questions, re-sat after every re-read. The score is on the public page. So is the list of questions it failed — an agent is told the gaps before it searches, which is the difference between a wrong answer and no answer.

3

It re-reads without you

Pages are checked by content hash; what changed is replaced. A release does not need a documentation sprint to reach the agents answering about you.

4

It reports what it was asked

Searches that found nothing become exam questions on their own. What your users could not get an answer to is a list you can read — and it is written by their agents, not by a survey.

What this does not do

It will not get you cited by ChatGPT.Nobody can sell you that, and the tools that do are selling a correlation. What we affect is what an agent gets when it calls a tool — which is a door we can actually hold open, and the only one.
llms.txt is not the fix it is sold as.Adoption sits around ~10% of scanned domains, and among the 50 most AI-cited domains exactly 1 has one. Google has confirmed no Search system reads it. It is genuinely useful for one thing — coding assistants fetching your docs cheaply at answer time — which is this door, not that claim.
It does not touch your search ranking.Different mechanism, different page, and anyone bundling the two has not measured either.
Most of the traffic cannot be attributed, and we say so.Around 70.6% of visits arriving from an AI assistant carry no referrer at all (measured across 446,000 visits) and land in every analytics tool as “direct”. We show what the first-touch tag could see and label the rest unrecorded, rather than dividing the unknown among the channels that happen to be measurable.
A low score gets published too.The exam is not a badge you buy. If the material is thin, the number says so on your own page — that is what makes the number worth anything when it is high.

Three steps, and the first one is a URL

Paste the link to your documentation. Every page behind it is found and read, the material becomes notes, and the brain sits its first exam — you read the score and the failed questions, add whatever they name, and it re-sits. Then you publish it, or keep it private and hand it only to your own team.
Today there are 166 public brains, and agents ran 9042 searches through mozg in the last thirty days.

Start with your docs URLHow a good one is builtAsk a person first

Sources for the three numbers above, none of them ours: llms.txt adoption and citation share — Presenc AI, “State of llms.txt 2026”, and OpenHermit's 2026 guide; Google's position stated by John Mueller; referrer loss on AI-assistant traffic — Cometly's 2026 tracking analysis. Read August 2026. The catalogue counts on this page are read from our own database when you load it.