Connect Text Readability Scorer MCP for AI Agents

The tool gives you objective, quantifiable scores like Flesch-Kincaid and Gunning Fog. You can use the consensus score to target a specific US grade level that data suggests will maximize your organic reach.

Yes, it's perfect for this. Legal teams can run policies through the MCP to verify they meet mandated readability levels (like 6th grade), significantly reducing confusion and risk.

They are different formulas, each measuring complexity using a unique linguistic method. The MCP provides both, allowing you to cross-reference results for a comprehensive evaluation of your text.

No. It moves beyond 'good' or 'bad.' It gives measurable data points—specific grade levels and complexity indices—that allow you to fix the content until it hits a precise, actionable target.

Absolutely. If your audience is non-technical or needs quick instructions, running the text through the scorer will pinpoint exactly where jargon is making it too difficult to follow.

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.