• SCORE
  • AUDIT
  • REVISE
  • HUMAN

How to calculate reading grade levels with your AI

Connect this MCP to your AI client to run precise readability formulas on your text. Your agent handles the math while you focus on the content.

Ask AI about this page

Short answer

How can I find the reading grade level of my text using AI?

Your AI uses specific formulas like Flesch-Kincaid or Gunning Fog to provide exact grade level scores. It calculates these metrics based on word length and sentence structure to give you a hard number. You'll see the specific score for every piece of text you submit.

The results you get.

Readability outcomes.

Once your AI processes your text, it delivers specific readability data.

SCORE

Exact grade levels

The AI returns a specific number representing the difficulty of your text. You get a clear grade level for both Flesch-Kincaid and Gunning Fog metrics.

AUDIT

Complexity checks

Your agent compares your text against target requirements. This tells you if your writing is too dense or too simple for your intended audience.

REVISE

Content adjustments

Use the scores to guide your edits. You can ask your AI to rewrite sections to hit a specific grade level target.

HUMAN

Final tone check

The AI provides the math, but you decide if the tone actually feels right. You must verify that the calculated complexity matches the intended reader experience.

The workflow

What your AI does when text arrives.

The AI moves from raw text to a mathematical readability score through a few specific steps.

  1. Analyze text

    Your agent reads the provided text to count syllables, words, and sentence lengths.

    calculate_flesch_kincaid

  2. Run Flesch-Kincaid

    The AI applies the Flesch-Kincaid formula to determine the grade level based on sentence and word complexity.

    calculate_flesch_kincaid

  3. Run Gunning Fog

    The agent uses the Gunning Fog index to provide a second perspective on how difficult the text is to read.

    calculate_gunning_fog

  4. Report scores

    The AI presents the final grade level results directly in your chat interface.

    calculate_flesch_kincaidcalculate_gunning_fog

Try it

Copy these to start.

Use these prompts to trigger the readability tools immediately.

Starting points

Swap the bracketed text with your actual content or file references.

Deterministic Readability Scorer Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_xn51InkPXPF6UvzWpq82RMb2AMpXMdXdY0wxIUyC/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — Deterministic Readability Scorer capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "deterministic-readability-scorer-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_xn51InkPXPF6UvzWpq82RMb2AMpXMdXdY0wxIUyC/mcp"
    }
  }
}
Copy into chat04

  • Calculate the Flesch-Kincaid grade level for this email: [paste text]

  • What is the Gunning Fog score for the documentation in this repo?

  • Check the readability of this blog post draft: [paste text]

  • Run both Flesch-Kincaid and Gunning Fog on the content in this Google Doc.

  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Start here

Connect Deterministic Readability Scorer once, then ask.

Click the link to connect the MCP to your client. Once connected, your AI can run these calculations on any text you provide.

Connect Deterministic Readability Scorer to your AI

FAQ

How the scorer behaves

  • 01

    Can the AI rewrite my text to be simpler?

    No. The tools only calculate the scores. You must ask your AI to perform the actual rewriting after it gives you the results.

  • 02

    Does the AI change my original document?

    No. The AI only reads the text to perform calculations. It does not have the capability to modify your files or documents.

  • 03

    What is the difference between the two scores?

    Flesch-Kincaid and Gunning Fog use different mathematical weights for sentence length and complex words. Using both gives you a more accurate picture of readability.

  • 04

    Can I use this with Claude or Cursor?

    Yes. Any MCP-compatible client like Claude, Cursor, or Windsurf can use this once you connect it through Vinkius.

  • 05

    Does the AI know if my writing is 'good'?

    No. The AI only knows if the text meets specific mathematical complexity thresholds. You are responsible for judging the quality and style.

  • More questions about Deterministic Readability Scorer? The Connector page answers them. See everything the Deterministic Readability Scorer Connector can do

Connect Deterministic Readability Scorer to Claude, Cursor, ChatGPT & more