Chi-Square Test Engine MCP for AI. Prove if your variables are statistically related.
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Chi-Square Test Engine runs exact Chi-Square independence tests on categorical data tables locally. You input observed counts, and this MCP returns guaranteed chi² statistics and p-values for rigorous statistical analysis—without relying on an LLM's math.
What your AI can do
Calculate chi square
Performs exact chi-Square tests of independence on categorical data tables, eliminating math hallucinations from LLMs.
Calculates the chi-square statistic to prove if two variables are statistically related or independent.
Builds the entire expected frequency matrix internally based on your observed data input.
Provides the precise p-value, letting you determine if a result is due to chance or a genuine pattern.
Supports contingency tables of any size, from simple 2x2 matrices up through larger data sets.
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Chi-Square Test Engine with 1 Tool
This single tool lets you perform exact, reliable chi-square tests of independence using observed frequency data.
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Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Chi-Square Test Engine on VinkiusCalculate Chi Square
Performs exact chi-Square tests of independence on categorical data tables, eliminating math hallucinations from LLMs.
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Claude AI
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 1 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Manually checking statistical dependencies is slow and prone to error.
Today, proving if your survey results are random chance or real trends means juggling spreadsheets. You're manually calculating expected frequencies, trying to keep track of degrees of freedom, and then nervously hoping the final p-value you calculate is right. It’s tedious, time-consuming, and one misplaced comma can invalidate weeks of work.
With this MCP, you send your raw observed counts once. The engine handles all the complex matrix math locally on your CPU. You get a clean report with the exact chi² statistic and p-value—it's done right, every time.
calculate_chi_square gives you definitive statistical proof.
You skip the multi-step calculation process entirely. You don't need to worry about whether your agent remembered to calculate the expected matrix or if it used the right formula for the degrees of freedom.
The difference is certainty. Instead of guessing, you get a deterministic output that proves whether two categories are linked or not.
What your AI can actually do with this
You need to know if two groups are actually related, or if their differences are just random chance. Trying to calculate expected frequencies or summing residuals in a large language model is risky; you risk getting hallucinated results that look authoritative but aren’t mathematically sound. This MCP fixes that. It computes the full statistical test deterministically using guaranteed math on your CPU.
You send it an observed frequency matrix, and it calculates everything: the exact expected counts, the chi² statistic, degrees of freedom, and the p-value. The whole thing runs locally, so your sensitive survey or business data never leaves your environment. Connecting to this MCP through Vinkius gives you reliable statistical proof for any categorical analysis, letting your agent focus on interpretation instead of calculation.
019e3876-cc2c-701e-acf0-13246d752948 
Here's how it actually works
The bottom line is you get mathematically verified proof about data relationships, every time.
You feed the MCP an observed frequency matrix showing counts across two categorical variables.
The engine runs a deterministic calculation locally on your CPU, generating expected frequencies and computing all necessary statistical metrics.
Your agent receives a clean report containing the chi² statistic, degrees of freedom, and the associated p-value.
Who is this actually for?
Data Scientists and Market Researchers who spend hours validating if survey results are statistically significant. It’s for the analyst tired of manually checking calculations or second-guessing an LLM's math.
Needs to prove that a change in marketing strategy genuinely moved sales, not just random fluctuation.
Must determine if customer complaint distribution depends on the product category or if they are truly independent variables.
Requires deterministic, auditable statistical output for models and hypothesis testing before presenting findings to stakeholders.
What Changes When You Connect
Stop trusting AI math. The calculate_chi_square tool runs the entire statistical test locally, guaranteeing you get precise chi² statistics and p-values every time.
Analyze complex relationships without worrying about data privacy. Your survey or business tables stay local on your CPU, so sensitive information is never exposed to an external cloud endpoint.
The engine automatically builds the full expected matrix for any size table (2x2, 3x3, etc.). You just provide the observed counts; it handles the rest of the heavy lifting.
Move past 'maybe' conclusions. Use the precise p-value output to tell stakeholders with confidence whether a relationship is statistically significant or just coincidence.
This MCP processes categorical data directly, making it perfect for A/B test results and survey cross-tabulations where independence testing is key.
See it in action
Determining if education level affects voting preference
A researcher inputs a matrix of vote counts by education level. The MCP returns the p-value, confirming that since p < 0.05, education level and voting preference are not independent.
Checking if complaint distribution varies by product line
A support manager feeds in customer complaint counts across different products. The MCP returns a high p-value, showing that the complaints appear independent of the product category and aren't worth acting on.
Validating A/B Test results for feature adoption
You feed in user counts comparing two groups (A vs B) across two outcomes (clicked vs didn't click). The MCP calculates the chi² statistic to see if the difference is genuine or just chance.
Assessing gender and subscription tier relationships
You run a test on user counts comparing gender groups against premium/basic subscriptions. The result shows statistical significance, proving that gender and subscription tier are definitely related.
The honest tradeoffs
Asking an LLM to calculate the statistics
Prompting your agent: 'Calculate the expected chi-square value for these 10x10 counts.' The AI generates a number that looks correct but is mathematically inaccurate or incomplete.
Send the observed data directly to the calculate_chi_square tool. It guarantees deterministic math, giving you accurate statistics and p-values every time.
Using continuous variables for the test
Trying to run a chi-square test on raw revenue amounts or age ranges because they aren't in neat categories.
Chi-Square only works with counts (categorical data). You must first group your data into distinct categories, then use calculate_chi_square.
Overlooking the null hypothesis
Seeing a low p-value and assuming it's always a 'yes.' Failing to recognize that failing to reject the null hypothesis still means there isn't enough evidence.
Always read the accompanying analysis. The MCP tells you if you can reject the null hypothesis, giving clear statistical interpretation alongside the math.
When It Fits, When It Doesn't
Use this MCP when your specific question is: Are these two categorical variables independent of each other? If you are comparing proportions across discrete groups—like gender vs. subscription tier, or complaint type vs. product line—this tool is mandatory. Don't use it if you need to model continuous data (e.g., predicting salary based on age); for that, you need a different regression-type MCP. Also, don't assume this tool handles all math; it only calculates the chi-square test. You still need to interpret the p-value and draw conclusions yourself.
Questions you might have
What is a contingency table? +
It's a matrix showing the frequency distribution of two categorical variables (e.g., rows = Gender, columns = Subscription Tier). The AI will automatically convert your raw data into this format.
Does it handle expected frequencies below 5? +
The engine computes the result regardless, but the AI is instructed to warn you when expected frequencies are low, as the chi² approximation becomes less reliable in those cases.
Can it test more than two variables at once? +
This engine performs a single pairwise independence test per execution. For multi-variable analysis, the AI can chain multiple calls to test different variable pairs sequentially.
How does `calculate_chi_square` ensure that my sensitive survey data stays private? +
The calculation runs locally on your CPU. Your observed frequency matrix and resulting statistics never leave your secure environment, keeping your business data confidential.
Is the result from `calculate_chi_square` deterministic and reliable compared to LLM math? +
Yes, it uses jstat for exact statistical computation. This means you get deterministically calculated chi² statistics and p-values, eliminating the risk of mathematical hallucinations.
What specific metrics does `calculate_chi_square` provide when I run a test? +
The engine returns three key values: the chi² statistic, the degrees of freedom (df), and the corresponding p-value. These are essential for determining statistical significance.
What format does `calculate_chi_square` require when I provide it with my data? +
It requires an observed frequency matrix, which is a structured representation of your contingency table (e.g., rows and columns detailing counts). The tool builds the expected frequencies internally.
How does `calculate_chi_square` handle different sizes of contingency tables? +
It supports any size matrix, from simple 2x2 tables up to larger dimensions like 3x3 or bigger. You don't have to limit your data just because the tool handles multiple variables.
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