text-readability Connector for AI agents.

It provides objective scores that correlate with search engine preferences for readable content. You can quickly identify and simplify dense sections to help reduce your bounce rate.

Yes, it is perfect for verifying that terms of service or privacy policies are actually readable for your customers and meet specific grade-level requirements.

It is a standard formula that tells you what US grade level a piece of text corresponds to. This helps you ensure your content is reaching the right audience.

Yes, it can analyze any amount of text you provide, from short social media posts to long-form white papers or medical articles.

It runs multiple algorithms like Gunning Fog and SMOG simultaneously and then gives you a consensus score to provide a more accurate picture of your text's difficulty.

AI models are great at writing, but they aren't good at counting syllables or calculating complex linguistic formulas. This capability provides the mathematical proof you need for professional audits.

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.

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.

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.