Claude CodeCLI

Text Readability Scorer MCP Server

Bring Linguistics
to Claude Code

Name: Text Readability Scorer MCP Server
Price: 29 USD
Availability: InStock
Rating: 2.2 (1 reviews)
Author: Vinkius

Learn how to connect Text Readability Scorer to Claude Code and start using 1 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.

GDPR Free for Subscribers

Readability Scorer

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Compatible with every major AI agent and IDE

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

+ other MCP clients

What is the Text Readability Scorer MCP Server?

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.

Built-in capabilities (1)

readability_scorer

Essential for SEO, marketing, and legal compliance. Calculate rigorous readability metrics for any text (Flesch-Kincaid, Gunning Fog, SMOG, etc.)

Why Claude Code?

Claude Code registers Text Readability Scorer as an MCP server in a single terminal command. Once connected, Claude Code discovers all 1 tools at runtime and can call them headlessly. ideal for CI/CD pipelines, cron jobs, and automated workflows where Text Readability Scorer data drives decisions without human intervention.

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Single-command setup: claude mcp add registers the server instantly. no config files to edit or applications to restart
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Terminal-native workflow means MCP tools integrate seamlessly into shell scripts, CI/CD pipelines, and automated DevOps tasks
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Claude Code runs headlessly, enabling unattended batch processing using Text Readability Scorer tools in cron jobs or deployment scripts
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Built by the same team that created the MCP protocol, ensuring first-class compatibility and the fastest adoption of new protocol features

See it in action

Text Readability Scorer in Claude Code

AI Agent→Vinkius

High Security·Kill Switch·Plug and Play

Why Vinkius

Text Readability Scorer and 4,000+ other MCP servers. One platform. One governance layer.

Teams that connect Text Readability Scorer to Claude Code through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.

4,000+MCP Servers ready

<40msCold start

60%Token savings

	Raw MCP	Vinkius
Server catalog	Find and host yourself	4,000+ managed
Infrastructure	Self-hosted	Sandboxed V8 isolates
Credential handling	Plaintext in config	Vault + runtime injection
Data loss prevention	None	Configurable DLP policies
Kill switch	None	Global instant shutdown
Financial circuit breakers	None	Per-server limits + alerts
Audit trail	None	Ed25519 signed logs
SIEM log streaming	None	Splunk, Datadog, Webhook
Honeytokens	None	Canary alerts on leak
Custom domains	Not applicable	DNS challenge verified
GDPR compliance	Manual effort	Automated purge + export

Unlock for AI Agents View step-by-step setup guide for Claude Code →

Enterprise Security

Why teams choose Vinkius for Text Readability Scorer in Claude Code

The Text Readability Scorer MCP Server runs on Vinkius-managed infrastructure inside AWS — a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts. All 1 tools execute in hardened sandboxes optimized for native MCP execution.

Your AI agents in Claude Code only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.

Unlock for AI Agents View full Text Readability Scorer details →

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

The Vinkius Advantage

How Vinkius secures
Text Readability Scorer for Claude Code

Every tool call from Claude Code to the Text Readability Scorer MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.

< 40msCold start

Ed25519Signed audit chain

60%Token savings

FAQ

Frequently asked questions

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.