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Connect Text Readability Scorer to ChatGPT and Claude

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. Get mathematical proof of your text's reading level.

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.

ChatGPTClaudeCursorPerplexityGeminiMicrosoft CopilotRaycastMeta AI

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.

A futuristic MCP client docking bay
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 · 1 capability

We ship only the actions that matter.

Capability set01 / 01

01

1 capability in this set.

Part of 1 available through Text Readability Scorer.

  1. 01

    Readability scorer

    This capability calculates rigorous readability metrics like Flesch-Kincaid and SMOG for any text you provide. It is built for checking SEO, marketing clarity, and legal compliance.

Real work

What teams actually run

01

SEO Optimization

Check if your blog posts are too complex for your target search audience.

02

Marketing Conversion

Ensure landing page copy stays at an accessible grade level to reduce bounce rates.

03

Legal Compliance

Verify that terms of service or privacy policies meet specific readability mandates.

04

Academic Review

Measure the complexity of research papers or medical articles using the SMOG index.

Automate a task

Put Text 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

Text 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.

ClaudeClaudeChatGPTChatGPTGeminiGeminiPerplexityPerplexityGrokGrokMicrosoft CopilotMicrosoft CopilotCursorCursorVS CodeVS CodeWindsurfWindsurfJetBrainsJetBrainsClineClineLangChainLangChainVercel AI SDKVercel AI SDKLovableLovableZ.aiZ.aiRaycastRaycastQwen CodeQwen CodeKimi CodeKimi CodeLe ChatLe Chat

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

You don't need a client to put Text 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

954ms average. Fast in production.

Text Readability Scorer is checked daily against the live service.

Daily averagePeak 1359ms
Oct 3Today
Fastest day
773ms
Slowest day
1359ms
14-day trend
Slowing+16%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 1 capability arrives 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 Text Readability Scorer, so you can see the experience inside your AI.

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

Text Readability Scorer Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_W9oYvTG1iKIoemfx3IYYV7gFBzYooLvoTGNLAkwk/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 — Text Readability Scorer capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "text-readability-scorer-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_W9oYvTG1iKIoemfx3IYYV7gFBzYooLvoTGNLAkwk/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 Text Readability Scorer in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Text Readability Scorer owners hand off.

Content professionals and compliance officers who need to move past subjective feedback and use data to guide their writing.

  • 01

    SEO Specialists

    They hand off drafts to the agent to ensure content hits the specific readability targets required for search rankings.

  • 02

    Copywriters

    They use the capability to verify that marketing copy is simple enough for their intended consumer base.

  • 03

    Legal Professionals

    They task the agent with checking that complex terms of service meet required readability standards.

FAQ

Questions Text Readability Scorer owners ask.

  • 01

    Can my AI agent accurately count syllables?

    No, LLMs struggle with syllable counting. This MCP uses the text-readability library to perform these calculations mathematically.

  • 02

    Which readability formulas are supported?

    The MCP supports Flesch-Kincaid, Gunning Fog, SMOG, Coleman-Liau, and the Automated Readability Index (ARI).

  • 03

    How do I use this with Claude or Cursor?

    Vinkius hosts the MCP for you. You simply connect your client to the Vinkius catalog to start using the capabilities immediately.

  • 04

    What is the Flesch-Kincaid Grade Level?

    It is an industry standard that returns a number corresponding to a US grade level, such as 8.2 for 8th grade.

  • 05

    Is this useful for SEO?

    Yes. It allows you to ensure your content matches the reading level of your target audience, which helps manage bounce rates.

  • 06

    Why can't the LLM just estimate the reading level?

    Readability formulas (like Flesch-Kincaid) require exact mathematical counts of syllables per word and words per sentence. LLMs operate on sub-word tokens, not syllables, making them notoriously bad at these calculations. This engine uses deterministic linguistic math.

  • 07

    What is a good Flesch Reading Ease score for web content?

    For general consumer web content, aim for 60-70. This translates to an 8th-9th grade reading level, which is easily understood by 80% of adults. Legal or academic texts usually score in the 30s or lower.

  • 08

    Does this work for non-English text?

    The formulas (Flesch, Fog, SMOG) were developed and calibrated specifically for the English language based on English syllable structures. While the engine will calculate a score for other languages, the grade-level mapping is only statistically accurate for English.

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