Coleman-Liau Readability Index Calculator

x402

Coleman-Liau Readability Index Calculator is a paid API for AI agents from kihustle.tech, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-10).

Computes the Coleman-Liau readability index score for a given text, estimating the US grade level needed to comprehend it.

Price / call$0.002

Seller volume →—

Success—

StatusUnknown

How this service works

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Output

Returns a JSON object with a result field containing the computed Coleman-Liau index score (a numeric value representing the approximate US school grade level required to understand the text) and a status field confirming successful processing.

Example use cases

Blog post grade level check

“Check the Coleman-Liau readability score for this blog intro I wrote: 'Understanding compound interest is essential for anyone looking to grow their savings over time. By reinvesting your returns, your balance grows exponentially rather than linearly.'”

Academic content difficulty assessment

“I'm writing educational material for middle schoolers — can you run the Coleman-Liau index on this passage to see if it's appropriate: 'Photosynthesis is the process by which plants convert sunlight, water, and carbon dioxide into glucose and oxygen using chlorophyll in their cells.'”

Marketing copy readability audit

“Run a Coleman-Liau readability check on this product description so I can make sure it's accessible: 'Our proprietary nano-filtration membrane leverages hydrophilic polymer matrices to achieve sub-micron particulate removal at ambient pressure differentials.'”

Technical details

Endpoint

POSThttps://kihustle.tech/nexus/api/v1/coleman-liau-index?utm_source=zero.xyz

When to prefer

Choose this endpoint when you specifically need the Coleman-Liau readability formula, which is based on characters-per-word and sentences-per-100-words (unlike Flesch-Kincaid which uses syllables). It is ideal when syllable counting is impractical or unreliable, or when you want a character-based readability metric compatible with automated text pipelines.

Side effects

Empty or missing text field returns an error or unexpected result
Extremely short text (fewer than a few words) may produce unreliable or out-of-range scores
Non-English text may yield inaccurate scores since the formula is calibrated for English
Very long text strings may cause timeout or truncation issues
Malformed JSON request body returns a 400 error

Input

Schema · Request body

Field	Type	Description
text	string

Output

Schema · Response

{
  "type": "json",
  "example": {
    "result": "processed",
    "status": "success"
  }
}

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