Specificity Score Checker
Paste a draft. See whether it has enough concrete detail to publish.
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See the generic version
This strips names, numbers, source cues, dates, and specific verbs with dumb deterministic rules. If it reads broken and empty, good — that is the point.
Your generic version scored —
What got removed
Specificity is not proof of truth or authorship.
This checker flags whether a draft contains named examples, numbers, source cues, and concrete details. It does not verify whether claims are true. Use Deep Scan, source review, or human editorial review for provenance-sensitive workflows.
Plain-English method
Specificity is a deterministic semantic-concreteness signal. It rewards claims that include names, numbers, dates, locations, examples, and source cues, then penalizes generic generalizations and vague wording.
Mechanism and scoring
The score is a weighted feature pass over parsed sentences and spans. It creates a generic weakened version to show how much meaning collapses when specifics are removed. This is useful for routing and revision, not truth verification.
This is one signal in a layered stack.
Single-method detectors are too easy to overtrust. Specificity is useful when it changes routing: allow, revise, human_review, or reject. It should be layered with specificity, provenance, pattern pressure, Unicode sanitation, media provenance, and paid Deep Scan when the decision matters.
check_specificity is available through local MCP with no LLM cost, and through the remote MCP endpoint with free unauthenticated rate limits. See all detection methodologies and the dedicated methodology page for the deepest treatment of this signal.