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Text Readability Scorer MCP Server for CursorGive Cursor instant access to 1 tools to Readability Scorer

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Cursor is an AI-first code editor built on VS Code that integrates LLM-powered coding assistance directly into the development workflow. Its Agent mode enables autonomous multi-step coding tasks, and MCP support lets agents access external data sources and APIs during code generation.

Ask AI about this MCP Server for Cursor

The Text Readability Scorer MCP Server for Cursor is a standout in the Productivity category — giving your AI agent 1 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

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JetBrainsJetBrains
VercelVercel
+ other MCP clients
Classic Setup·json
{
  "mcpServers": {
    "text-readability-scorer": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}
RecommendedModern Approach — Zero Configuration

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The modern way to manage MCP Servers — no config files, no terminal commands. Install Text Readability Scorer and 4,000+ MCP Servers from a single visual interface.

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Text Readability Scorer
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High SecurityEnterprise-grade
IAMAccess control
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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

About 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.

Cursor's Agent mode turns Text Readability Scorer into an in-editor superpower. Ask Cursor to generate code using live data from Text Readability Scorer and it fetches, processes, and writes. all in a single agentic loop. 1 tools appear alongside file editing and terminal access, creating a unified development environment grounded in real-time information.

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.

The Text Readability Scorer MCP Server exposes 1 tools through the Vinkius. Connect it to Cursor in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 1 Text Readability Scorer tools available for Cursor

When Cursor connects to Text Readability Scorer through Vinkius, your AI agent gets direct access to every tool listed below — spanning linguistics, readability-metrics, text-analysis, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

readability

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.)

Connect Text Readability Scorer to Cursor via MCP

Follow these steps to wire Text Readability Scorer into Cursor. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

01

Open MCP Settings

Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
02

Add the server config

Paste the JSON configuration above into the mcp.json file that opens
03

Save the file

Cursor will automatically detect the new MCP server
04

Start using Text Readability Scorer

Open Agent mode in chat and ask: "Using Text Readability Scorer, help me...". 1 tools available

Why Use Cursor with the Text Readability Scorer MCP Server

Cursor AI Code Editor provides unique advantages when paired with Text Readability Scorer through the Model Context Protocol.

01

Agent mode turns Cursor into an autonomous coding assistant that can read files, run commands, and call MCP tools without switching context

02

Cursor's Composer feature can generate entire files using real-time data fetched through MCP. no copy-pasting from external dashboards

03

MCP tools appear alongside built-in tools like file reading and terminal access, creating a unified agentic environment

04

VS Code extension compatibility means your existing workflow, keybindings, and extensions all work alongside MCP tools

Text Readability Scorer + Cursor Use Cases

Practical scenarios where Cursor combined with the Text Readability Scorer MCP Server delivers measurable value.

01

Code generation with live data: ask Cursor to generate a security report module using live DNS and subdomain data fetched through MCP

02

Automated documentation: have Cursor query your API's tool schemas and generate TypeScript interfaces or OpenAPI specs automatically

03

Infrastructure-as-code: Cursor can fetch domain configurations and generate corresponding Terraform or CloudFormation templates

04

Test scaffolding: ask Cursor to pull real API responses via MCP and generate unit test fixtures from actual data

Example Prompts for Text Readability Scorer in Cursor

Ready-to-use prompts you can give your Cursor agent to start working with Text Readability Scorer immediately.

01

"Analyze this landing page copy. We need it to be at an 8th-grade reading level to maximize conversions."

02

"Our legal team says the new Terms of Service must be readable by a 6th grader. Verify the text."

03

"Check the SMOG Index and Gunning Fog for this medical article before we publish it."

Troubleshooting Text Readability Scorer MCP Server with Cursor

Common issues when connecting Text Readability Scorer to Cursor through Vinkius, and how to resolve them.

01

Tools not appearing in Cursor

Ensure you are in Agent mode (not Ask mode). MCP tools only work in Agent mode.
02

Server shows as disconnected

Check Settings → Features → MCP and verify the server status. Try clicking the refresh button.

Text Readability Scorer + Cursor FAQ

Common questions about integrating Text Readability Scorer MCP Server with Cursor.

01

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.
02

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.
03

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.
04

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.

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