Why shouldn't I just ask my AI to calculate readability?

AI models process text as tokens, not syllables. This leads to mathematical hallucinations when they try to do linguistic math. This MCP uses a Javascript engine to provide the actual, correct numbers.

What is the difference between Flesch-Kincaid and Gunning Fog?

Flesch-Kincaid focuses on grade level and reading ease based on sentence and word length. Gunning Fog specifically looks at the density of complex, polysyllabic words.

Can I change the reading speed for time estimates?

Yes. The calculate_reading_time capability allows you to provide a custom Words Per Minute (WPM) value. It defaults to 200 WPM if you don't specify one.

Does this MCP require any external dependencies?

No. It uses a zero-dependency architecture with a pure Javascript runtime for fast, reliable processing.

Which AI clients can use this MCP?

Any MCP-compatible client, including Claude, Cursor, Windsurf, and VS Code, can use this capability.

Why do AI models fail at calculating readability scores?

Readability formulas require knowing the exact number of phonetic syllables. LLMs process text in semantic tokens (e.g., 'unbelievable' might be 2 tokens, but it has 5 syllables). They cannot count syllables accurately, making algorithmic capabilities mandatory.

Does it support multiple languages?

The syllable counting heuristic is highly optimized for English, which is the baseline for Flesch-Kincaid. However, the reading time and basic word/sentence extraction work flawlessly across all Latin-script languages.

Are there any external library dependencies?

No. We utilize a custom Regular Expression syllable engine built natively into the TypeScript architecture, achieving 0ms latency processing without downloading external NLP packages.

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Connect Deterministic Readability Scorer to your AI

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Stop letting your AI guess linguistic complexity.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 3 capabilities

We ship only the actions that matter.

Capability set01 / 01

01-03

3 capabilities in this set.

Part of 3 available through Deterministic Readability Scorer.

01
Calculate gunning fog
This capability uses the Gunning Fog index algorithm to measure text complexity. It scans for polysyllabic words to determine how difficult a text is to read.
02
Calculate flesch kincaid
This capability provides mathematically perfect Flesch-Kincaid Reading Ease and Grade Level scores. It works by extracting exact syllable, word, and sentence counts.
03
Calculate reading time
This capability calculates the exact chronological reading time for a piece of text. You can specify a custom Words Per Minute (WPM) speed to get a precise minute and second estimate.

Real work

What teams actually run

Content Accessibility Audits

Run a legal document through the Gunning Fog capability to see if it's too dense for a general audience.

Newsletter Planning

Use the reading time capability to tell subscribers exactly how long a new article will take to read.

Educational Material Verification

Check that textbook content stays within a specific Flesch-Kincaid grade level for students.

UX Writing Optimization

Ensure microcopy in apps remains simple and easy to digest using readability metrics.

Automate a task

Put Deterministic Readability Scorer to work on one thing.

Step-by-step automations your AI runs through this Connector: the workflow, the prompts to start with, and the exact capabilities behind each move.

One connector, every AI

Deterministic Readability Scorer works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

Building your own app? The connector is yours to use.

You don't need a client to put Deterministic Readability Scorer to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

942ms average. Fast in production.

Deterministic Readability Scorer is checked daily against the live service.

Daily averagePeak 1327ms

Oct 3Today

Fastest day: 805ms
Slowest day: 1327ms
14-day trend: Improving-7%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 3 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Deterministic Readability Scorer, so you can see the experience inside your AI.

It does not authenticate your account with Deterministic Readability Scorer. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

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.

1In Claude Desktop, open Settings → Connectors.
2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
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"
    }
  }
}

Claude
ChatGPT
Cursor
VS Code
Windsurf
Claude Code
JetBrains
Cline

Step-by-step instructions for each client are in the guide. How to connect

Guided setup for Claude? Set up Deterministic Readability Scorer in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Deterministic Readability Scorer owners hand off.

This MCP is built for professionals who need to verify the accessibility and complexity of written content.

01
Technical Writers
They hand off documentation to the agent to ensure it meets specific grade level requirements for users.
02
Copywriters
They use the agent to check if marketing copy is too complex for their target audience.
03
Editors
They use the agent to get exact reading time estimates for newsletter and blog planning.

FAQ

Questions Deterministic Readability Scorer owners ask.

01
Why shouldn't I just ask my AI to calculate readability?
AI models process text as tokens, not syllables. This leads to mathematical hallucinations when they try to do linguistic math. This MCP uses a Javascript engine to provide the actual, correct numbers.
02
What is the difference between Flesch-Kincaid and Gunning Fog?
Flesch-Kincaid focuses on grade level and reading ease based on sentence and word length. Gunning Fog specifically looks at the density of complex, polysyllabic words.
03
Can I change the reading speed for time estimates?
Yes. The calculate_reading_time capability allows you to provide a custom Words Per Minute (WPM) value. It defaults to 200 WPM if you don't specify one.
04
Does this MCP require any external dependencies?
No. It uses a zero-dependency architecture with a pure Javascript runtime for fast, reliable processing.
05
Which AI clients can use this MCP?
Any MCP-compatible client, including Claude, Cursor, Windsurf, and VS Code, can use this capability.
06
Why do AI models fail at calculating readability scores?
Readability formulas require knowing the exact number of phonetic syllables. LLMs process text in semantic tokens (e.g., 'unbelievable' might be 2 tokens, but it has 5 syllables). They cannot count syllables accurately, making algorithmic capabilities mandatory.
07
Does it support multiple languages?
The syllable counting heuristic is highly optimized for English, which is the baseline for Flesch-Kincaid. However, the reading time and basic word/sentence extraction work flawlessly across all Latin-script languages.
08
Are there any external library dependencies?
No. We utilize a custom Regular Expression syllable engine built natively into the TypeScript architecture, achieving 0ms latency processing without downloading external NLP packages.

Explore

More in Productivity

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

Your AI does the work. We keep it safe and sound.

Each Connector runs inside its own sealed environment.

One switch stops every agent. Instantly.

Spending limits that pause instead of surprise.

We ship only the actions that matter.

01-03

Calculate gunning fog

Calculate flesch kincaid

Calculate reading time

What teams actually run

Content Accessibility Audits

Newsletter Planning

Educational Material Verification

UX Writing Optimization

Building your own app? The connector is yours to use.

Deterministic Readability Scorer Connector

Claude Desktop

Technical Writers

Copywriters

Editors

Why shouldn't I just ask my AI to calculate readability?

What is the difference between Flesch-Kincaid and Gunning Fog?

Can I change the reading speed for time estimates?

Does this MCP require any external dependencies?

Which AI clients can use this MCP?

Why do AI models fail at calculating readability scores?

Does it support multiple languages?

Are there any external library dependencies?

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