Skip to content
Vinkius

simple-statistics Connector for AI agents.

1 live capability

Verify data distributions for accurate statistical testing.

Live agent request simple-statistics / Connector

Waiting for input…

AI Agent

Why people use simple-statistics

Normality Test Engine for Reliable Statistical Analysis

This Connector lets your agent do the heavy lifting. Just hand the agent your data and it handles the Jarque-Bera test instantly. You get a clear verdict and the exact stats you need without ever leaving your chat interface.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • Visual Studio Code
  • Windsurf

What Vinkius changes

You get a mathematically sound go or no-go for your statistical tests instead of an AI guess.

Use it from Claude, ChatGPT, Cursor or another AI client you already have.

One account · 5,900+ Connectors

  1. Real-world use case 01

    Regression Analysis

    A researcher wants to check if their residuals are normal before trusting their linear model.

  2. Real-world use case 02

    Survey Validation

    A sociologist needs to know if survey scores are normally distributed before running an ANOVA.

  3. Real-world use case 03

    Sensor Data

    An engineer wants to see if noisy sensor data is normal enough for standard deviation-based alerts.

Complete set · 1capability

The complete simple-statistics capability set.

These are the exact actions your AI can choose when you ask it to work with simple-statistics.

Capability set01 / 01

01

1 capability in this set.

Part of 1 available through simple-statistics.

  1. 01 Capability

    Test normality

    Runs a Jarque-Bera test on your numbers to check for normality. It gives you a p-value and a clear pass/fail verdict for your data.

Set up in minutes

One URL. Then ask simple-statistics to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use simple-statistics from the conversation.

Choose your client

Live preview
Advanced clients IDE · CLI

Claude · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP
https://edge.vinkius.com/vk_preview_WUlfcwTW8ijdRBrYjBDTpDiFw1dcKsTz5HdufzbG/mcp
  1. Step 01

    Open Connectors

    In Claude Web or Claude Desktop, open Settings and choose Connectors.

  2. Step 02

    Add the URL

    Choose Add custom connector, name it simple-statistics, and paste the URL above.

  3. Step 03

    Turn it on in chat

    Select +, open Connectors, and enable simple-statistics for the conversation.

Where the request belongs

Work simple-statistics can move forward.

Built around the request

This is for the data scientist or academic researcher who can't afford to publish flawed results. It's for anyone whose work relies on parametric statistics where close enough isn't an option.

01

Data Scientist

Checking residuals for regression models to ensure the underlying assumptions are met.

02

Academic Researcher

Validating survey scores or experimental data before running an ANOVA.

03

Financial Analyst

Verifying returns distributions for risk models to prevent using incorrect standard formulas.

Build the capability set

Add more capabilities.

Each Connector adds new actions and data without changing how you work.

Browse Connectors
T-Test Statistics Engine logo
01 1 capability

T-Test Statistics Engine

Run exact Student's, Welch's, and Paired t-tests local. Get CPU-guaranteed p-values instead of LLM-hallucinated guesses.

View Connector
Outlier Detection Engine logo
02 1 capability

Outlier Detection Engine

Identify statistical anomalies in massive datasets local using deterministic Z-Score and IQR methods. Stop LLMs from guessing which rows are outliers.

View Connector
ANOVA Calculator Engine logo
03 1 capability

ANOVA Calculator Engine

Run exact One-Way ANOVA tests to compare means across multiple groups local. Get CPU-guaranteed F-scores and p-values, not LLM guesses.

View Connector
Statistics Engine logo
04 5 capabilities

Statistics Engine

A zero-latency statistical engine to process datasets. Instantly compute the exact mean, median, mode, standard deviation, and percentiles completely local.

View Connector
Data Analysis Prover logo
05 1 capability

Data Analysis Prover

A marketing team asked an AI to analyze campaign data. The AI reported 'significant correlation between email frequency and purchase rate (p<0.05).' The team tripled emails. Unsubscribes spiked 340%. Sample: N=47 self-selected respondents, no power analysis. Correlation: observational, no confounders. Distribution: right-skewed but mean used. p=0.043 but Cohen's d=0.12. trivial. Chart: truncated Y-axis making a 2% difference look enormous. This capability forces five axes: sample validity, causal inference, distribution awareness, significance with effect size, and visualization integrity.

View Connector
Chi-Square Test Engine logo
06 1 capability

Chi-Square Test Engine

Run exact Chi-Square independence tests on contingency tables local. Get CPU-guaranteed chi² statistics and p-values for categorical analysis.

View Connector

Bring your own AI

Change the model, client or framework. Keep simple-statistics connected.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • VS Code
  • Windsurf
  • ZCode
  • Cline
  • Zed
  • Continue
  • Kiro
  • Roo Code
  • Zencoder
  • Goose
  • Void
  • Augment Code
  • Amp
  • Qodo
  • Tabnine
  • Pieces
  • Sourcegraph Cody
  • JetBrains
  • Warp
  • Amazon Q
  • Antigravity
  • BoltAI
  • Raycast
  • Jan
  • LM Studio
  • AnythingLLM
  • Open WebUI
  • Msty
  • Cherry Studio
  • LibreChat
  • TypingMind
  • Chorus
  • 5ire
  • n8n
  • LangChain
  • LlamaIndex
  • CrewAI
  • Vercel AI SDK

Before you connect

Questions about simple-statistics.

The practical details behind the request, access and result.

What is the Normality Test Engine MCP for?

It helps your AI agent check if your numeric data follows a normal distribution. This is a required step before you can use many standard statistical tests like ANOVA or t-tests.

Does the Normality Test Engine MCP use my data for training?

No. All calculations are performed locally on your machine. Your research data stays private and is never sent to an external server.

How does the Normality Test Engine MCP handle math?

It uses the simple-statistics library to calculate exact coefficients. This prevents the AI from hallucinating numbers and gives you a mathematically accurate result.

Can I use the Normality Test Engine MCP for non-numeric data?

No, this capability is specifically for numeric data. It calculates skewness and kurtosis, which require numbers to function correctly.

What is a Jarque-Bera test?

It's a statistical test used to determine if data has the properties of a normal distribution. This Connector runs that test for you to give a clear pass/fail verdict.

Why can't I just ask the AI if the data looks normal?

LLMs are not calculators. They might guess correctly sometimes, but they often get it wrong. This Connector provides a deterministic, local calculation you can actually trust.

Is this the Shapiro-Wilk test?

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.

How many data points do I need?

The Jarque-Bera test works best with 30 or more samples. For very small samples (n < 20), consider using visual QQ-plot analysis as a complement.

What does a 'not normal' result mean for my analysis?

If your data is not normally distributed, parametric tests like t-tests and ANOVA may be unreliable. Consider using non-parametric alternatives like Spearman correlation or Mann-Whitney U tests.

One connection away

Give your agent a direct line to simple-statistics.

Connect simple-statistics once. Keep it beside 5,900+ managed Connectors when the next task needs more.

Explore every Connector No credit card required · Free tier available