# Text Readability Scorer MCP > The `readability_scorer` tool calculates mathematical readability metrics (Flesch-Kincaid, Gunning Fog, SMOG) for any text input. It tells you the exact US grade level needed to understand your copy, moving beyond vague AI 'feelings' to give concrete scores for SEO and compliance. ## Overview - **Category:** productivity - **Price:** Free - **Tags:** linguistics, readability-metrics, text-analysis, seo-optimization, compliance-checking, natural-language-processing ## Description You're running copy that sounds great to you, but is it actually easy for people to read? Don't trust an LLM just because it says the tone is 'engaging.' Those things are vague. If your text isn't simple enough, your bounce rate jumps up—period. You gotta have math on your side to prove how hard your writing is. The `readability_scorer` tool doesn't guess; it runs established linguistic formulas. It gives you concrete scores that publishing houses and SEO teams actually use. When you run this server through your AI client, you immediately get a full breakdown of your text’s complexity using metrics like Flesch-Kincaid, Gunning Fog, and SMOG. **How the `readability_scorer` Works** The tool's job is to process any piece of copy and tell you exactly what kind of education someone needs to understand it. It’s designed for anyone who can't afford guesswork on their content strategy. * **Determine US Grade Level:** The most critical function here is calculating the Flesch-Kincaid grade level. This metric spits out a number that corresponds directly to a U.S. grade of schooling—for example, an 8.2 means the average eighth grader should be able to follow it. You use this score every time you need to know if your copy hits its target audience right on point. * **Measure Reading Difficulty (0-100):** It also returns a Flesch Reading Ease score, which runs from 0 up to 100. Here’s the deal: higher numbers mean simpler stuff for mass consumption. If you're writing something meant for everyone—like a basic FAQ or consumer guide—you want that number high. The metric tells you if your copy is accessible enough for broad market appeal. * **Calculate Compliance Scores:** For legal, medical, or highly regulated industries, compliance matters. This tool runs rigorous metrics like SMOG and Gunning Fog. These scores verify whether the text meets specific industry standards or legal requirements for clarity. They give you measurable proof that your document is compliant, not just 'pretty enough.' * **Compare Multiple Metrics:** You don't have to run four different checks on separate platforms. The `readability_scorer` simultaneously processes several algorithms—including Flesch-Kincaid, SMOG, and ARI. This lets you compare how different mathematical metrics score the same piece of writing, giving you a comprehensive view without switching tools. When your AI client uses this server, it aggregates these multiple results instantly. You don't get a pile of numbers to cross-reference; you get actionable data that shows exactly where your text falls on the difficulty spectrum. It tells you what changes you need to make—whether that means simplifying jargon or beefing up technical details—to hit your specific audience goal. ## Tools ### readability_scorer Calculates rigorous and mathematically accurate readability scores (Flesch-Kincaid, Gunning Fog, SMOG) for any text input to assess complexity. ## Prompt Examples **Prompt:** ``` Analyze this landing page copy. We need it to be at an 8th-grade reading level to maximize conversions. ``` **Response:** ``` Flesch-Kincaid Grade: 11.4 | Reading Ease: 42.1 | Consensus: 11th and 12th grade. Too complex. Needs simplification. ``` **Prompt:** ``` Our legal team says the new Terms of Service must be readable by a 6th grader. Verify the text. ``` **Response:** ``` Flesch-Kincaid Grade: 6.2 | Reading Ease: 78.5 | Meets compliance requirement for 6th-grade readability. ``` **Prompt:** ``` Check the SMOG Index and Gunning Fog for this medical article before we publish it. ``` **Response:** ``` SMOG: 14.2 | Gunning Fog: 15.8 | Highly academic. Requires college-level reading comprehension. ``` ## Capabilities ### Determine US Grade Level The tool calculates the Flesch-Kincaid grade level, telling you what actual U.S. grade of education is required to understand the text. ### Measure Reading Difficulty (0-100) It returns a numerical score via Flesch Reading Ease, where higher numbers indicate simpler copy for mass consumption. ### Calculate Compliance Scores The tool runs metrics like SMOG and Gunning Fog to verify if the text meets specific legal or industry readability standards. ### Compare Multiple Metrics It simultaneously processes several algorithms (Flesch-Kincaid, SMOG, ARI) so you can compare how different metrics score the same piece of writing. ## Use Cases ### Auditing a High-Stakes Landing Page A marketer uploads new landing page copy and asks their agent to check its readability. The agent uses `readability_scorer` and reports: 'Flesch-Kincaid Grade: 12.5.' The marketer knows the target audience is college-educated, but not academic; they must simplify complex sentences. ### Verifying Legal Compliance The legal team drafts a new policy document and asks their agent to run it through `readability_scorer`. The tool returns 'Flesch-Kincaid Grade: 6.1,' confirming the text passes internal compliance checks for clarity. ### Optimizing Blog Content Flow A technical writer has a dense article and wants to break it up. They send the original draft to their agent, which uses `readability_scorer` to find sections that score over 10th grade. The writer then rewrites those specific passages for simpler language. ### A/B Testing Copy Clarity A copywriter has two versions of a sales pitch (A and B). They send both to their agent, which runs `readability_scorer` on each. The tool might show Version A is 9th grade while Version B is 7th grade—the clear winner. ## Benefits - Stop guessing if your copy is simple enough. The `readability_scorer` gives you specific scores for Flesch-Kincaid and Gunning Fog, letting you confirm compliance instead of hoping it reads well. - Improve SEO performance by targeting the right difficulty level. You can analyze a draft and immediately see how far off your current score is from the optimal grade level required for high organic traffic. - Pass legal review instantly. Need to prove your Terms of Service are readable by a 6th grader? Run them through `readability_scorer` and get mathematical confirmation that they meet regulatory standards. - Compare algorithms side-by-side. Since the tool runs SMOG, ARI, and others alongside Flesch-Kincaid, you see if your text is difficult for one reason (like long words) but easy for another. - Avoid mobile bounce rate spikes. If your copy scores too high on complexity, users leave. Use `readability_scorer` to simplify the language until it's universally digestible. ## How It Works The bottom line is you get hard numbers proving how difficult your text is to read, which lets you fix it before publishing. 1. You pass the text block—the article copy, legal terms, or landing page content—to your AI client and request a readability analysis. 2. The `readability_scorer` tool executes standard linguistic formulas on that specific text input. 3. Your agent receives an output listing multiple scores (e.g., Flesch-Kincaid: 10.5, Gunning Fog: 12) and a consensus score. ## 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. **What are the input limitations when using the `readability_scorer` tool?** The tool handles large text blocks, making it useful for analyzing full articles or white papers. While there isn't a strict character limit on the server side, extremely massive inputs might trigger platform rate limits instead of the scoring function. **If I pass blank or empty text to `readability_scorer`, what does it return?** It returns a structured output containing null or zero metrics for all algorithms. The system doesn't throw an error; instead, you get placeholder values for Flesch-Kincaid, Gunning Fog, and SMOG. **Is the content I analyze using Text Readability Scorer kept private?** Yes. Vinkius manages secure connections for all MCP calls, ensuring your input text remains confidential. The copy passed to `readability_scorer` is not retained or used for model training. **How can I ensure the output from `readability_scorer` is structured and machine-readable?** The tool provides clear, distinct metrics separated by algorithm. When connecting it via agents like Pydantic AI, you can force a precise JSON schema for guaranteed data parsing. **Does the `readability_scorer` require any local setup or authentication keys?** No local setup is needed on your end. You simply connect your preferred AI client to the Text Readability Scorer MCP endpoint and pass the required text data directly in the prompt payload.