Llama tone analyzer

One passage, read for the features that carry an impression: sentence length and how much it varies, hedging and modality, whether requests are framed as questions or instructions, punctuation placement, the order in which people and tasks appear, and whether any of that shifts partway through.

The output is a written reading capped at 1,400 completion tokens, produced at a low temperature so repeat runs converge on the same substance. It is a description of the text, not a measurement of it, and the distinction determines how much weight the result can carry.

This category contains two incompatible architectures sold under one name, and the difference is not marketing. One computes; the other interprets. A lexicon scorer looks each word up in a rated list, most commonly one where every English word carries a value from minus five to plus five, and returns arithmetic. It is deterministic, instant, can run entirely in a browser and is blind to everything above the word.

A model reading takes the passage as a whole. It handles negation, context and structure that a word list cannot, and it gives up the properties that made the word list trustworthy: exact reproducibility, a stable scale, and in our case local processing. Neither is better in the abstract. What matters is knowing which one produced the answer in front of you.

Lexicon scorer

Reproducible, numeric, often local and unlimited. Cannot see negation, context or sarcasm.

Model reading

Follows structure and context, returns prose. Not reproducible to the word, no scale, and on this route not local.

This route

A model reading. No lexicon, no score, no fixed label set, one report per run.

Served remotely. The passage is posted to our AI server, read, and the report returned in the same response.

Step 1

Paste 1 to 12,000 characters, including any prior message the tone is responding to.

Step 2

Supply the reader and the intended effect in up to 300 characters, which is what turns a generic reading into a check against intent.

Step 3

Locate each finding in the passage before acting on it. A finding you cannot point at in the text is one to discard.

Tone is rarely produced by the words people worry about. It emerges from structural choices that the writer usually made without deciding anything, which is why rereading your own message stops working after the third pass.

Naming the signal is more useful than naming the tone, because the signal is what you can change. A message described as cold gives you nothing to edit. A message where every request is an imperative and no sentence exceeds nine words gives you two specific things to edit.

Hedging density

Too little reads as blunt, too much reads as evasive. The comfortable band is narrow and audience dependent.

Sentence length variance

Uniformly short declaratives read as curt regardless of content.

Request framing

Question, instruction or statement of need. The same request lands three different ways.

Order of mention

Whether the message opens with the person or the task sets the frame for everything after it.

A number here would have to come from somewhere. Competing tools take theirs from a rated word list and can say exactly how it was derived, which makes their score honest even where it is literal. This route has no lexicon and no classifier, so any figure displayed would be a model's impression of a number, which is the least trustworthy object in the whole category.

The same reasoning rules out an emotion label from a fixed set and a per sentence breakdown. Both imply a classification step that does not exist here. One report, describing one passage, with the signals named so you can check them yourself.

Lexicon tools publish their inability to detect sarcasm, and they are right to. A model reading does better on inversion signalled inside the passage, and it does not solve the problem, because the signal for irony often sits outside the text entirely: in what happened last week, in the relationship, in a tone of voice that was never written down.

The wider version of that limit applies to every tone tool. Tone is a property of a reading, not only of a text, so the honest output is a description of risk. This clause can be read as an ultimatum. This opening can be read as dismissive. Nothing here claims to know what a writer felt, and treating a report about a message as evidence about a person is a misuse of it.

Five runs per rolling 24 hours against a hashed identifier. Twelve thousand characters per run. One passage at a time, with no batch mode, no API, no browser extension and no editor integration. No rewriting of any kind, because this route is a classifier rather than a rewriter.

The text is transmitted to our AI server. Several competitors in this result set run entirely in the browser and transmit nothing, which is a real advantage for private correspondence and is stated here rather than glossed over. The local tools on this site are the watermark and hidden character routes, and this is not one of them.

Communications staff checking that a policy notice reads as neutral rather than defensive. Managers writing performance feedback where precision about the effect matters more than the vocabulary. Writers working across a language gap, where formality overshoots in both directions and neither error is visible from inside.

In all three, the request is the same: name the mechanism, not the mood. A reader can dispute a mechanism by looking at the sentence. A mood label offers nothing to argue with, which is exactly why it is less useful than it looks.