Tone & Readability Dashboard
Audit structural voice attributes and complexity indices of computational texts.
Paste any text above to generate a full readability and tone audit.
About This Tool
The Tone & Readability Dashboard is a comprehensive linguistic audit tool for anyone who writes or evaluates text — content strategists, prompt engineers, technical writers, researchers, and editors. It runs your text through six established readability formulas and a suite of voice attribute heuristics to produce a structured, actionable report on how your text reads and how it sounds.
Readability indices (Flesch-Kincaid, Gunning Fog, SMOG, Coleman-Liau, ARI) measure how cognitively demanding your text is for a reader. Voice attributes — sentiment polarity, formality level, hedging density, passive voice ratio, lexical density, and personal pronoun usage — reveal the structural personality of the writing. The built-in Highlight Explorer lets you visually scan the text for hedge words, complex words, positive phrases, and negative phrases in-context.
How to Use
- Paste any text into the input field — article, AI output, email, essay, research paper, or prompt. Analysis updates live as you type.
- Read the Summary Hero — four headline metrics: Reading Ease score, average Grade Level, Sentiment polarity, and Formality level.
- Inspect Readability Indices — six formulas each measure a different structural dimension of complexity. Compare them to get a consensus grade level.
- Read the Voice Bars — seven tone gauges show passive voice, hedging, lexical density, pronoun usage, and more as percentages.
- Use Highlight Explorer — switch between highlight modes (Hedge Words, Complex Words, Positive, Negative) to visually locate problematic patterns directly in your text.
- Copy Report — export a structured plain-text report of all scores to your clipboard.
Common Use Cases & FAQ
A Quick Note
All analysis runs 100% client-side in your browser. No text is sent to any server. Readability scores are standard formula implementations and are calibrated for English-language text.
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