Is My Prompt Clear Enough?
Answer the question directly: a clarity score plus the specific reasons a prompt reads as unclear — vague terms, unstated objective, missing specifics.
Measure a prompt's structure — headings, lists, sentence length, density — to see whether its shape helps the model or buries the instructions.
A long prompt with no structure asks the model to parse a wall of text, and instructions get lost in it. This loads exactly that — one enormous run-on sentence packing decisions, action items, owners, and tone into a single breath — and measures the structural facts: no headings, no lists, a very high average sentence length. Structure is a fact, not a quality judgment; the analysis reports the shape and notes where it may reduce clarity. It measures structure; it does not reshape it.
Paste the prompt
Especially a long or wall-of-text one.
Read the structural facts
Headings, lists, paragraphs, average sentence length.
See where shape hurts
Notes flag long sentences and missing structure.
Measure only — it's a read-only diagnosis and "the prompt is not modified." On the loaded 87-word single-sentence prompt it reports the structural facts: 0 sections, 0 list items, average sentence length 87.0 words, and a Structure score of 30/100. The prompt-readability-checker surfaces the shape; its own note points you to the Prompt Formatter to reshape it.
It counts how many of six concrete anchors — number, length limit, format, audience, examples, criteria — the prompt contains; the loaded example scores 0 of 6, which is why Specificity reads 0/100. The OBSERVATION NOTES flag that no output length or format was detected. Structure is reported as a fact, not a quality verdict, and the checker doesn't guarantee it caught every clarity issue.
Answer the question directly: a clarity score plus the specific reasons a prompt reads as unclear — vague terms, unstated objective, missing specifics.
Diagnose a prompt before you send it: explainable scores for clarity, ambiguity, specificity, and structure, with observations that name where it is vague.
Surface the ambiguity a model will exploit: vague quantities, hedges, undefined time, open-ended lists, and conflicting instructions — flagged, not fixed.
A set of before-and-after examples showing exactly what prompt cleanup removes — and what it deliberately leaves alone.
'Make it good', 'be detailed', 'keep it interesting' — vague prompts get vague output. The fix is mechanical: replace every fuzzy word with a checkable instruction.
Formats fuzzy agent instructions into a structured prompt with objective, available tools, constraints, success criteria, and failure handling.
Diagnose a prompt's clarity before you send it — explainable scores for clarity, ambiguity, specificity, and structure. Read-only; it never rewrites.