MCP For Claude Marketplace
Normality Test Engine

Normality Test Engine MCP Connector for Claude

A+

Test whether your data is normally distributed using Skewness and Kurtosis analysis local. Essential pre-check before running parametric statistical tests.

1 tools Official Updated Jun 28, 2026 Official Vinkius Partner
More Details Connect to Claude

Before running t-tests, ANOVA, or linear regression, you need to verify that your data is normally distributed. LLMs cannot eyeball a distribution from raw numbers — they will guess and often guess wrong.

This MCP uses simple-statistics to compute exact Skewness and Kurtosis coefficients, then applies a Jarque-Bera test to determine normality. The AI gets a definitive pass/fail verdict with the exact test statistic and p-value.

The Superpowers

  • Zero Hallucination: Exact statistical coefficients computed locally.
  • Automated Verdict: Returns a clear 'normal' or 'not normal' interpretation.
  • Descriptive Statistics: Also provides exact Mean, Std Dev, Skewness, and Kurtosis.
  • Data Privacy: Your research data stays entirely on your local machine.
statisticsdata-sciencenormality-testmathematical-analysisdata-validation

1 tools expose this connector's capabilities to your AI agent.

test_normality

Perform an exact deterministic Jarque-Bera normality test on numeric data without LLM math hallucinations

See how to talk to your AI agent using Normality Test Engine.

Check if this residuals array is normally distributed before I run my regression.

The Jarque-Bera test returns p = 0.42. We fail to reject the normality hypothesis — your residuals appear normally distributed. Proceed with the regression.

Is this sensor data normally distributed or should I use a non-parametric test?

The data shows significant positive skewness (1.8) and high kurtosis (5.2). The Jarque-Bera p-value is 0.001 — the data is not normally distributed. Use a non-parametric test.

Run a normality test on the 'Revenue' column before I calculate confidence intervals.

Normality test passed (p = 0.61). Skewness is 0.3 and kurtosis is 2.9 — both close to normal parameters. Your confidence intervals will be valid.

This engine implements the Jarque-Bera normality test, which uses Skewness and Kurtosis. It is highly effective for medium-to-large samples and avoids the Shapiro-Wilk implementation gaps in JavaScript.

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