Claude CodeCLI

Deterministic Readability Scorer MCP Server

Bring Text Analysis
to Claude Code

Name: Deterministic Readability Scorer MCP Server
Price: 29 USD
Availability: InStock
Rating: 0.2 (1 reviews)
Author: Vinkius

Learn how to connect Deterministic Readability Scorer to Claude Code and start using 3 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.

GDPR Free for Subscribers

Calculate Flesch KincaidCalculate Gunning FogCalculate Reading Time

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What is the Deterministic Readability Scorer MCP Server?

AI models perceive text as 'tokens', not as phonetic syllables or strict sentence boundaries. Because of this, asking an LLM to calculate a Flesch-Kincaid readability score directly will always result in a mathematical hallucination. The Readability Scorer MCP solves this by routing text analysis through a deterministic V8 Javascript engine.

The Superpowers

Flesch-Kincaid Precision: Automatically extracts total syllables, words, and sentences to provide mathematically perfect Reading Ease and Grade Level scores.
Gunning Fog Index: Determines the complexity of your text by algorithmically scanning for polysyllabic words (3+ syllables).
Exact Reading Time: Instead of guessing, it calculates the exact chronological reading time (minutes and seconds) based on a configurable WPM (Words Per Minute).
Zero-Dependency Architecture: Pure Javascript runtime execution means absolute processing speed with no external bloated packages.

Built-in capabilities (3)

calculate_flesch_kincaid

Provide the full text string. Analyzes text readability using the deterministic Flesch-Kincaid algorithm

calculate_gunning_fog

Provide the full text string. Analyzes text readability using the deterministic Gunning Fog index algorithm

calculate_reading_time

Provide the text and optionally the Words Per Minute (WPM) speed (defaults to 200). Provides an exact reading time estimation based on word count and WPM

Why Claude Code?

Claude Code registers Deterministic Readability Scorer as an MCP server in a single terminal command. Once connected, Claude Code discovers all 3 tools at runtime and can call them headlessly. ideal for CI/CD pipelines, cron jobs, and automated workflows where Deterministic 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 Deterministic 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

Deterministic Readability Scorer in Claude Code

AI Agent→Vinkius

High Security·Kill Switch·Plug and Play

Why Vinkius

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

Teams that connect Deterministic 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 Deterministic Readability Scorer in Claude Code

The Deterministic 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 3 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 Deterministic 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
Deterministic Readability Scorer for Claude Code

Every tool call from Claude Code to the Deterministic 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 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 tools 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.