CursorIDE

Deterministic Readability Scorer MCP Server

Bring Text Analysis
to Cursor

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 Cursor 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 Cursor?

Cursor's Agent mode turns Deterministic Readability Scorer into an in-editor superpower. Ask Cursor to generate code using live data from Deterministic Readability Scorer and it fetches, processes, and writes. all in a single agentic loop. 3 tools appear alongside file editing and terminal access, creating a unified development environment grounded in real-time information.

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Agent mode turns Cursor into an autonomous coding assistant that can read files, run commands, and call MCP tools without switching context
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Cursor's Composer feature can generate entire files using real-time data fetched through MCP. no copy-pasting from external dashboards
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MCP tools appear alongside built-in tools like file reading and terminal access, creating a unified agentic environment
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VS Code extension compatibility means your existing workflow, keybindings, and extensions all work alongside MCP tools

See it in action

Deterministic Readability Scorer in Cursor

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 Cursor 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 Cursor →

Enterprise Security

Why teams choose Vinkius for Deterministic Readability Scorer in Cursor

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 Cursor 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 Cursor

Every tool call from Cursor 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.

What is Agent mode and why does it matter for MCP?

Agent mode is Cursor's autonomous execution mode where the AI can perform multi-step tasks: reading files, editing code, running terminal commands, and calling MCP tools. Without Agent mode, Cursor operates in a simpler ask-and-answer mode that doesn't support tool calling. Always ensure you're in Agent mode when working with MCP servers.

Where does Cursor store MCP configuration?

Cursor looks for MCP server configurations in a mcp.json file. You can configure servers at the project level (.cursor/mcp.json in your project root) or globally (~/.cursor/mcp.json). Project-level configs take precedence.

Can Cursor use MCP tools in inline edits?

No. MCP tools are only available in Agent mode through the chat panel. Inline completions and Tab suggestions do not trigger MCP tool calls. This is by design. tool calls require user visibility and approval.

How do I verify MCP tools are loaded?

Open Settings → Features → MCP and look for your server name. A green indicator means the server is connected. You can also check Agent mode's available tools by clicking the tools dropdown in the chat panel.