Text Readability Score API

POST

Know the reading grade level and target audience of any text.

Text Readability Score calculates the readability score and grade level of any text across standard formulas. It returns Flesch Reading Ease, Gunning Fog, Coleman-Liau, syllable and word counts, an average grade level, and a target audience label.

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Request
POSTapi.apiverve.com/v1/readabilityscore
Body
Verification
Format

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Example
{
  "status": "ok",
  "error": null,
  "data": {
    "textCounts": {
      "syllableCount": 82,
      "lexiconCount": 49,
      "sentenceCount": 1
    },
    "readability": {
      "fleschReadingEase": 13.28,
      "fleschKincaidGrade": 23.6,
      "gunningFog": 26.95,
      "colemanLiauIndex": 13.19,
      "smogIndex": 22.08,
      "automatedReadabilityIndex": 27.8,
      "daleChallReadabilityScore": 17
    },
    "readabilityText": {
      "fleschReadingEase": "very confusing",
      "daleChallReadabilityScore": "average 13th to 15th-grade (college) student"
    },
    "averageGradeLevel": 22.7,
    "targetAudience": "post-graduate"
  }
}

About the Text Readability Score API

Readability Score works by analyzing the text provided and calculating the readability score. It uses advanced algorithms to calculate the readability score based on various readability formulas and returns the readability score.

What people use it for

Editorial CMS Publishing
Before publishing articles, newsroom CMS editors check the Flesch-Kincaid grade level and word counts to match target reader expectations.
Patient Instructions Review
When clinicians draft care summaries, health portals flag passages scoring above an elementary target audience so staff can rephrase dense medical terms.
Curriculum Reading Assessment
To assign books to suitable grade bands, edtech apps evaluate uploaded chapters against Dale-Chall scores and the average grade level.
Consumer Contract Auditing
Flag convoluted policy clauses during drafting by checking consumer agreements against the Gunning Fog index and reading ease ratings.

Ways to call it

One endpoint, many ways in — REST with JSON, XML, YAML and CSV, plus GraphQL and an MCP interface for AI agents.

JSON
Default REST response
XML
Markup format
YAML
Human-readable
CSV
Tabular export
Beta
GraphQL
Query language
New
MCP
For AI agents

Other ways to use Text Readability Score

Same data, same APIVerve account, same credit balance — one key works on all of them.

Questions.

Common questions about the Text Readability Score API.

Read the docs →
Which readability formulas are included in the analysis?
The API calculates scores across seven standard formulas: Flesch Reading Ease, Flesch-Kincaid Grade Level, Gunning Fog, Coleman-Liau Index, SMOG Index, Automated Readability Index, and the New Dale-Chall score. Each metric evaluates text difficulty using distinct structural or vocabulary rules.
Does it return plain-language ratings alongside the raw scores?
Yes. In addition to numeric values, you receive text interpretations such as difficulty labels derived from Flesch Reading Ease and Dale-Chall. It also assigns a target audience classification, such as elementary, high school, or post-graduate.
Does the response break down word and sentence counts?
Yes. Every response includes total syllable counts, word counts, and sentence counts detected in the analyzed text. These structural metrics show the exact quantities used by the readability formulas.
How is the overall grade level calculated?
The API computes an average grade level by averaging the grade-based metrics, including Flesch-Kincaid, Gunning Fog, Coleman-Liau, and SMOG. This gives you a single composite school-grade benchmark without requiring you to average the individual formulas manually.
Which plan fits analyzing 50,000 texts a month?
Starter easily covers that volume. Each analysis uses 2 credits, so 50,000 texts consume 100,000 credits, using half of Starter's 200,000 monthly credits. That volume works out to about $0.30 per 1,000 calls. The Free plan includes 100 calls per month.

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