Text Readability Scorer MCP Server with 1 Tools for Claude, Cursor, and AI Agents
GDPR Free for SubscribersCalculate mathematically accurate readability metrics (Flesch-Kincaid, Gunning Fog, SMOG) for any text. Stop relying on AI 'feelings' — get exact US grade levels for SEO and compliance. Vinkius routes your AI agents directly to Text Readability Scorer through a governed connection. 1 tools ready to use with Claude, ChatGPT, Cursor, or any AI agent — no hosting, no setup, connect in 30 seconds.
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What is the text-readability MCP Server?
The text-readability MCP Server routes AI agents like Claude, ChatGPT, and Cursor directly to text-readability via 1 tools. Calculate mathematically accurate readability metrics (Flesch-Kincaid, Gunning Fog, SMOG) for any text. Stop relying on AI 'feelings' — get exact US grade levels for SEO and compliance. Powered by Vinkius — your credentials stay on your side of the connection, every request is auditable. Connect in under 2 minutes.
Built-in capabilities (1)
Tools for your AI Agents to operate text-readability
Ask your AI agent "Analyze this landing page copy. We need it to be at an 8th-grade reading level to maximize conversions." and get the answer without opening a single dashboard. With 1 tools connected to real text-readability data, your agents reason over live information, cross-reference it with other MCP servers, and deliver insights you would spend hours assembling manually.
Works with Claude, ChatGPT, Cursor, and any MCP-compatible client. Powered by Vinkius — your credentials never touch the AI model, every request is auditable. Connect in under two minutes.
Why teams choose Vinkius
One subscription gives you the infrastructure to connect your AI agents to thousands of MCP servers — and deploy your own to the Vinkius Edge. Your credentials stay yours. Your data flows directly between your agent and the API. DLP blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade routing and governance, zero maintenance.
Build your own MCP Server with our secure development framework →The Text Readability Scorer App Connector works with every AI agent you already use
…and any MCP-compatible client
Use all 1 Text Readability Scorer tools with your AI agents right now
Vinkius routes your AI agents to Text Readability Scorer through a governed proxy. Beyond a simple connection, you get full visibility into every action your agents perform, with enterprise-grade security and up to 60% savings on AI costs.
Readability scorer on Text Readability Scorer
Essential for SEO, marketing, and legal compliance. Calculate rigorous readability metrics for any text (Flesch-Kincaid, Gunning Fog, SMOG, etc.)
What the Text Readability Scorer MCP Server unlocks
You ask your AI copywriter: 'Is this blog post easy to read?' It says 'Yes, it is very engaging!' Then you run it through a real SEO tool and it scores at a university reading level — killing your mobile bounce rate.
LLMs cannot accurately count syllables or calculate sentence complexity. This MCP uses the text-readability library to execute standard linguistic formulas, providing mathematical proof of how difficult your text is to read.
The Superpowers
- Flesch-Kincaid Grade Level: The industry standard. Returns a number corresponding to the US grade level (e.g., 8.2 = 8th grade).
- Flesch Reading Ease: A 0-100 scale where higher is easier. Essential for broad audience copy.
- Multiple Algorithms: Also calculates Gunning Fog, Coleman-Liau, SMOG, and Automated Readability Index (ARI).
- Consensus Evaluation: Automatically aggregates all scores to give you a definitive target audience level.
Frequently asked questions about the Text Readability Scorer MCP Server
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.
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.
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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We built the connector to Text Readability Scorer. Now put your agents to work. Fully governed.
Vinkius is the AI Gateway with managed hosting. Stop building connectors. Every connection runs inside eight layers of security.
Hosted, sandboxed, and live on AWS. You don't provision anything. You don't maintain anything. You connect.
Every tool call, every token, every response. Logged and auditable. Data flows direct from Text Readability Scorer to your agent. Nothing is stored on our side. Ever.
Eight governance layers on every request. Sensitive data redacted before it reaches the model. Kill switch if anything goes sideways. Always on.