Readability Scorer MCP for AI. Get mathematically precise content clarity scores.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Connect to your AI in seconds.
The Deterministic Readability Scorer calculates objective text metrics, bypassing common LLM errors. It runs mathematical analyses—like Flesch-Kincaid and Gunning Fog indexes—through a deterministic JavaScript engine.
This means you get mathematically precise readings of complexity, grade level, and exact reading time estimates every single time, regardless of how your AI agent interprets syllables or sentence boundaries.
What your AI can do
Calculate flesch kincaid
Runs the text through the Flesch-Kincaid algorithm to score its reading ease and grade level.
Calculate gunning fog
Analyzes the content using the Gunning Fog index, specifically flagging polysyllabic words for complexity.
Calculate reading time
Provides an accurate time estimate (minutes and seconds) based on the word count and a set Words Per Minute speed.
Analyze a text and determine its difficulty level using the Gunning Fog index.
Provide precise Flesch-Kincaid readings to see what educational level is required for comprehension.
Determine the exact number of minutes and seconds a user will take to read the text at a set speed.
Ask an AI about this
Waiting for input…
Deterministic Readability Scorer: 3 Tools
These three tools let you measure content complexity, estimate reading times, and score text against specific linguistic standards.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Deterministic Readability Scorer on VinkiusCalculate Flesch Kincaid
Runs the text through the Flesch-Kincaid algorithm to score its reading ease and grade level.
Calculate Gunning Fog
Analyzes the content using the Gunning Fog index, specifically flagging polysyllabic...
Calculate Reading Time
Provides an accurate time estimate (minutes and seconds) based on the word count and...
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Claude AI
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
Add Custom Connector
Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:
https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp
Replace [YOUR_TOKEN_HERE] with your token
from cloud.vinkius.com. For OAuth-protected servers, expand
Advanced settings to add credentials.
Start a conversation
Open a new chat. The Readability Scorer integration is available immediately — no restart needed.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Deterministic Readability Scorer, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by readability-scorer. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
VINKIUS INFRASTRUCTURE
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on every call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 3 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Manual Clarity Checks Are Time Sinks
Today, checking a document's readability feels like guesswork. You manually read through paragraphs, stopping to wonder if the phrasing was too dense or if the subject matter felt overwhelmingly technical. Then, you might try searching for 'readability score calculator,' leading you to dozens of conflicting web tools that give vague results—you're forced to copy-paste data into multiple places just to get a rough idea.
With this MCP, your agent handles the heavy lifting. You feed it the text once, and it calculates all necessary metrics in one go. You get precise, deterministic scores for complexity, grade level, and reading time estimates—all without leaving your workflow.
The Deterministic Readability Scorer
You don't have to manually copy text segments into three different websites or prompt your AI client repeatedly. The MCP bundles the entire analysis process, allowing you to check Flesch-Kincaid score, Gunning Fog index, and reading time with a single call.
The difference now is reliability. You move from 'this seems okay' to 'the data confirms it scores an 8th Grade Level,' giving you concrete proof of your content’s clarity.
What your AI can actually do with this
Writing for an audience isn't just about sounding smart; it’s about making sure the other person can actually understand what you wrote. But asking an LLM to calculate readability scores often results in inaccurate numbers because the model doesn't count words like a calculator does. This MCP fixes that problem by routing all text analysis through a stable, deterministic engine.
You feed it your copy, and it calculates three hard metrics: how complex the language is (Gunning Fog), what grade level someone needs to be to read it (Flesch-Kincaid), and exactly how long it'll take them to get through (reading time). This isn't guesswork; it’s precise linguistic math. By connecting this MCP via Vinkius, you give your AI client the ability to objectively grade your content against strict rules, making sure your message lands clearly every time.
019e38e0-cfea-70b4-bf67-52ffe65b9f57 
Here's how it actually works
The bottom line is that you get objective, mathematically guaranteed readability data points for your content.
Send your full draft or article text string to this MCP.
The underlying engine runs the text through deterministic JavaScript math, calculating word counts, syllable totals, and sentence boundaries.
Your AI client receives three precise metrics: the Flesch-Kincaid score, the Gunning Fog index, and an exact reading time estimate.
Who is this actually for?
This MCP is critical for technical writers, instructional designers, and marketing managers whose job relies on clarity. If you're tired of guessing if your documentation is too dense or if your client report is confusing, this tool gives you objective proof.
Needs to check complex API guides and manuals to ensure the material can be understood by non-expert developers.
Must validate training materials against specific educational standards, ensuring the content hits a defined comprehension level for new hires.
Validates blog posts and web copy to make sure they are accessible enough for a broad audience without sounding patronizing.
What Changes When You Connect
Avoid guesswork on audience comprehension. Instead of hoping your copy is clear, use the calculate_flesch_kincaid tool to get a definitive Grade Level score for your text.
Identify overly academic language before publishing. The calculate_gunning_fog tool flags high volumes of complex, multi-syllabic words that make content feel dense and hard to read.
Manage user expectations on content length. Use the calculate_reading_time tool to tell readers exactly how long they should expect the article to take, boosting engagement right out of the gate.
Bypass LLM math errors. Because this MCP runs analysis in a deterministic JavaScript engine, you get consistent, reliable scores every time—no hallucinations allowed.
Improve content for specific goals. You can adjust your writing style until both the Flesch-Kincaid score and the Gunning Fog index meet your target parameters.
See it in action
The compliance team needs to update a technical manual.
They draft a section explaining network protocols but realize it’s too dense. They run the text through calculate_gunning_fog and see the index is 18.2, indicating overly complex vocabulary. They rewrite paragraphs until the index drops below 12.
A marketer needs to write a blog post for new customers.
They draft content that sounds too academic. Using calculate_flesch_kincaid, they see a Grade Level of 10.5. They simplify the vocabulary and sentence structure until the score lands in the 7th-grade range, making it instantly approachable.
An editor reviews a newsletter for time commitment.
The copy is ready but feels long. Using calculate_reading_time with a standard WPM of 200, the agent reports the article will take 4 minutes and 15 seconds. The editor knows exactly how to segment the content.
An academic needs to write an abstract for a general audience.
They have highly complex research findings. By running the text through calculate_flesch_kincaid, they get a score too high for a public-facing summary. They then rewrite it until the grade level suggests clarity for high school students.
The honest tradeoffs
Asking an AI model to calculate scores.
You prompt your agent: 'What is the Flesch-Kincaid score of this text?' The agent replies with a number, but you suspect it's wrong because LLMs don't run linguistic math like that.
Always use the dedicated tools. Call calculate_flesch_kincaid directly to ensure the result is based on deterministic code, not conversational estimation.
Focusing only on one metric.
You fix the Gunning Fog score but notice the text still feels bulky and long. You're left with a single number that doesn't tell you the whole story.
Use all three tools together. Check calculate_gunning_fog for vocabulary density, then use calculate_flesch_kincaid for overall grade level, and finally verify the total time estimate via calculate_reading_time.
Copy-pasting raw text into an unfamiliar tool.
You paste a massive document into one of the tools without specifying which metric you need, resulting in confusing output or an error.
Always provide context when calling the tools. If you want to check for complexity, explicitly call calculate_gunning_fog with your text.
When It Fits, When It Doesn't
Use this MCP if your primary goal is objective measurement of content clarity. You need to know how difficult a piece of writing is, not just that it 'sounds good.' For instance, if you are translating internal legal documents for public consumption, calculate_gunning_fog tells you if the vocabulary is too dense; meanwhile, if you're publishing a quick newsletter update, calculate_reading_time tells you if it respects the reader’s time. Don't use this MCP just because your text feels 'academic.' If all you need is stylistic feedback or tone suggestions (e.g., 'make it friendlier'), then stick to general writing agents. But if clarity and measurable comprehension are non-negotiable, this combination of tools is what you need.
Questions you might have
How do I use calculate_flesch_kincaid for my blog? +
Simply provide the full text string to the tool. It will analyze the copy and return a mathematically precise Reading Ease and Grade Level score, telling you exactly who your audience is.
Is calculate_gunning_fog better than Flesch-Kincaid? +
They measure different things. Use calculate_gunning_fog when vocabulary complexity (long words) is the main concern, and use calculate_flesch_kincaid for a broader grade-level assessment.
What if my text is really long? How do I find out how long it will take to read? +
Use calculate_reading_time. You just need to provide the text and optionally set your target WPM speed, and it gives you the exact minutes and seconds.
Why do I need a deterministic scorer over my general AI agent? +
Because general agents might 'hallucinate' math. This MCP uses dedicated JavaScript code to guarantee the scores are mathematically accurate, not just conversationally plausible.
If I run `calculate_flesch_kincaid` multiple times, are there rate limits or performance concerns? +
The platform handles scaling automatically. We recommend batching your analysis requests rather than making rapid sequential calls. This approach minimizes overhead and ensures the fastest possible throughput.
What happens if I provide an empty string to `calculate_gunning_fog`? +
The tool handles this gracefully. It will return a specific null result and an error code, letting you know that no analysis was performed. This prevents runtime failures in your agent.
Can I use `calculate_reading_time` without specifying Words Per Minute (WPM)? +
Yes, the tool defaults to a 200 WPM speed. You can easily override this parameter if your target audience reads faster or slower than the standard default.
What format does the text need to be in for `calculate_flesch_kincaid`? +
It only requires a plain, unformatted UTF-8 string. You don't need to worry about HTML tags or specific markdown; just feed it the raw body of text.
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.
We've already built the connector for Readability Scorer. Just plug in your AI agents and start using Vinkius.
No hosting. No infrastructure. No complex setup.
All 3 tools are live and waiting.
You're up and running in seconds.
Gemini Vinkius gives your AI agents access to the full catalog of app connectors, all fully managed, secure, and enterprise-ready. One subscription, every tool you need.
Built, hosted, and secured by Vinkius. You just connect and go.