Readability Score Skill

Overview

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.

Live Test Readability Score Skill Skill →

The tool

Once your client is connected to the VerveKit server, this appears in its tool list as ReadabilityScoreSkill. It is read-only and open-world — it fetches and never mutates anything on your side — so most clients call it without asking you to confirm.

Tool call

{
  "name": "ReadabilityScoreSkill",
  "arguments": {
    "text": "The quick brown fox jumps over the lazy dog. This sentence contains every letter of the alphabet."
  }
}

You do not name the tool yourself; the model picks it. Asking about The quick brown fox jumps over the lazy dog. This sentence contains every letter of the alphabet. in the terms this skill covers is enough for it to reach for ReadabilityScoreSkill on its own — naming it explicitly also works, and is the way to force the call.

Connecting

One server URL covers every skill in the catalog, including this one. Authorization is OAuth: the client opens a browser once, and there is no key to paste into a config file.

{
  "mcpServers": {
    "vervekit": {
      "url": "https://api.vervekit.com/v1/mcp"
    }
  }
}

Per-client setup — Claude, Cursor, VS Code, ChatGPT — is on the MCP setup page.

Arguments

These are the properties on the tool's inputSchema, so a well-behaved client validates them before the call is made. Premium arguments are accepted on every plan but only take effect on plans that include them.

Argument	Type	Description
`text`Required	string	The text to calculate the readability score of

What the model gets back

The result carries a structuredContent object matching the tool's declared outputSchema, so a client reads fields without parsing prose. status is "ok" and error is null on success; a null field means the value was not available for that input, not that the call failed.

Result

{
  "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"
  }
}

Response fields

Paths are relative to data. Premium fields are absent rather than zeroed on plans that do not include them, so check for presence instead of comparing to 0.

Field	Type	Example	Description
`textCounts`	object	`{…}`	Counts of syllables, words, and sentences found in the analyzed text
`textCounts.syllableCount`	number	`82`	Total number of syllables detected in the text
`textCounts.lexiconCount`	number	`49`	Total number of words in the text
`textCounts.sentenceCount`	number	`1`	Total number of sentences detected in the text
`readability`	object	`{…}`	Raw numeric scores from each readability formula
`readability.fleschReadingEase`	number	`13.28`	Flesch Reading Ease score on a 0-100 scale; higher scores mean the text is easier to read
`readability.fleschKincaidGrade`	number	`23.6`	Flesch-Kincaid Grade Level; the approximate U.S. school grade a reader needs to understand the text
`readability.gunningFog`	number	`26.95`	Gunning Fog index; the estimated years of formal education a reader needs to understand the text on a first reading
`readability.colemanLiauIndex`	number	`13.19`	Coleman-Liau Index; an estimated U.S. grade level based on characters per word and words per sentence
`readability.smogIndex`	number	`22.08`	SMOG Index; the estimated years of education needed to understand the text, based on its count of polysyllabic words
`readability.automatedReadabilityIndex`	number	`27.8`	Automated Readability Index; an estimated U.S. grade level based on characters per word and words per sentence
`readability.daleChallReadabilityScore`	number	`17`	New Dale-Chall score; a readability rating based on how many words in the text are outside a list of words familiar to average 4th-grade readers
`readabilityText`	object	`{…}`	Plain-language interpretations of the numeric readability scores
`readabilityText.fleschReadingEase`	string	`very confusing`	Plain-language difficulty label derived from the Flesch Reading Ease score, e.g. "very easy" or "very confusing"
`readabilityText.daleChallReadabilityScore`	string	`average 13th to 15th-grade (college) student`	Plain-language grade-level description derived from the Dale-Chall score, e.g. "average 4th-grade student or lower"
`averageGradeLevel`	number	`22.7`	Average grade level computed by averaging the grade-based metrics (Flesch-Kincaid, Gunning Fog, Coleman-Liau, SMOG, and Automated Readability Index)
`targetAudience`	string	`post-graduate`	Suggested target audience derived from the average grade level, e.g. "elementary", "high school", or "post-graduate"

Failure modes

Errors come back as tool errors carrying a sentence the model can act on, not a bare status code. Error handling covers the full list.

Status	What it means
`400` / `422`	The arguments did not validate. The message names the offending one.
`401`	The OAuth session is invalid or expired — reconnect the server.
`403`	Blocked by a key restriction or an IP allow-list. Never a bad identity.
`404`	This skill is not part of VerveKit. Check the catalog.
`429`	Out of credits, or a brief rate limit. The message tells them apart.

A call costs 2 credits each time the tool actually runs; a model that reasons about the tool without calling it costs nothing.

Use cases

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.