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Vinkius

simple-statistics Connector for AI agents.

1 live capability

Get mathematically precise correlation coefficients for your research data.

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AI Agent

Why people use simple-statistics

Stop Trusting AI Math with the Correlation Matrix Engine

This Connector moves the math off the AI's brain and onto your local CPU. When you ask your agent to analyze your data, it calls the engine to do the actual heavy lifting. You get a perfectly accurate NxN matrix and a list of the top 5 strongest relationships delivered instantly, so you can actually trust the results.

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

What Vinkius changes

You get mathematically perfect correlation data without the risk of AI hallucinations.

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

One account · 5,900+ Connectors

  1. Real-world use case 01

    Housing Price Analysis

    A real estate analyst wants to see which features like square footage or age impact price.

  2. Real-world use case 02

    Clinical Trial Results

    A researcher needs to find relationships between dosage and patient response.

  3. Real-world use case 03

    Churn Prediction

    A marketing lead wants to know what drives customer cancellations.

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

    Calculate correlation matrix

    Calculates exact Pearson correlation matrices across multiple datasets offline. It provides a complete NxN table of coefficients to ensure your data analysis is mathematically accurate.

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_O1b8NjjanR8Sw2KCAIHEbQQ02N2GeBTrkdiOkUNP/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

Data scientists who need to validate hypotheses without manual Excel work, and researchers who need publication-ready statistics without trusting an LLM's math skills.

01

Data Scientist

Validates feature importance for machine learning models by checking which variables actually move the needle.

02

Academic Researcher

Calculates correlations for clinical trial results to ensure statistical significance before writing up findings.

03

Business Analyst

Identifies drivers of customer churn or sales trends by mapping out dozens of internal metrics at once.

Build the capability set

Add more capabilities.

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

Browse Connectors
Chi-Square Test Engine logo
01 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
ANOVA Calculator Engine logo
02 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.

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Statistics Engine logo
03 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
Time-Series Seasonality Engine logo
04 1 capability

Time-Series Seasonality Engine

Compute exact Autocorrelation (ACF) to find seasonality lags in time-series data without hallucination.

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Asset Correlation Matrix logo
05 3 capabilities

Asset Correlation Matrix

Calculate Pearson correlation between assets to identify diversification risks and hedging opportunities.

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Data Analysis Prover logo
06 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.

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Bring your own AI

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

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Before you connect

Questions about simple-statistics.

The practical details behind the request, access and result.

Can the Correlation Matrix Engine MCP handle large datasets?

Yes, it can calculate correlations across all numeric columns in your dataset, providing a complete NxN matrix regardless of how many variables you have.

Does this Connector keep my data private?

Absolutely. All calculations happen locally on your machine, meaning your sensitive data never has to be sent to a cloud server for processing.

What is the difference between Pearson and Spearman in this Connector?

Pearson measures linear relationships, while Spearman measures monotonic relationships based on ranks. This Connector supports both so you can choose the right math for your specific data type.

Will the AI hallucinate the numbers?

No, because the AI isn't doing the math. It sends the data to a dedicated local engine that uses CPU-computed coefficients for perfect precision.

Can I use this for non-numeric data?

This Connector is designed for numeric columns. It works by calculating coefficients between numbers, so it won't work for purely text-based categories.

How many correlations can it find at once?

It generates a full NxN matrix, which means it maps out every single possible pair of correlations between your columns in one go.

What is the difference between Pearson and Spearman?

Pearson measures linear relationships and assumes normally distributed data. Spearman is rank-based, making it robust against outliers and ideal for non-linear monotonic relationships.

How many columns can I correlate at once?

There is no hard limit. The engine builds the NxN matrix dynamically. The practical limit depends on the LLM's context window for serializing the input JSON.

Does it show which correlations are the strongest?

Yes! The engine automatically extracts and ranks the top 5 strongest absolute correlations, making it easy for the AI to highlight key insights.

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.

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