How to Use the Chi-Square Test Engine MCP in Claude Code

Q: How do I use the Chi-Square Test Engine with Claude Code in a shell script?

First, connect Claude Code to the server using the claude mcp add command. Then, in your script, you can pipe a JSON contingency table to claude code --tool calculatechisquare. The result is printed to stdout for you to use.

Q: Is there a specific command to connect the Chi-Square Test Engine to Claude Code?

Yes. You run claude mcp add --transport http chi-square-test -- , replacing with the server endpoint. You can then check that it's connected with claude mcp list.

Pipe exact Chi-Square test results into your scripts and CI/CD pipelines with Claude Code. No GUIs, no fuss, just reliable stats.

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Connect Chi-Square Test Engine MCP to Claude Code

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Setup Chi-Square Test Engine with Claude Code

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Automate Statistical Monitoring

Use the `calculate_chi_square` tool to run scheduled checks on your data. Since it's a deterministic calculation, you can trust the output for automated alerting. Pipe your data into Claude Code, have it call this tool, and then trigger an action if the p-value drops below your threshold. This turns statistical analysis into a reliable part of your infrastructure. You can set up a cron job to monitor A/B test results or check for data drift in production datasets. Claude Code and this MCP Server give you a simple, scriptable way to do it.

Add Guardrails to Data Pipelines

Integrate this tool directly into your CI/CD workflow for data validation. Before deploying a model or updating a report, have a step in your pipeline where Claude Code checks for unexpected shifts in data relationships. If a Chi-Square test fails your significance threshold, the build can fail automatically. It's a great way to catch data quality issues or real changes in user behavior before they hit production.

A Focused, Headless MCP Server

This MCP tool is built for automation. It takes a contingency table and returns a chi-squared statistic and a p-value. That's it. There are no complex objects to parse or weird side effects. The simple, predictable output is perfect for shell scripting. You can easily pipe the JSON result to `jq`, check the p-value, and branch your script's logic accordingly. It's a command-line-native approach to stats.

Setup guide

Set up Chi-Square Test Engine MCP in Claude Code

Prerequisites

Claude Code CLI installed (npm install -g @anthropic-ai/claude-code)
Active Vinkius subscription with a valid endpoint token

1

Run the add command

Open your terminal and run the command shown on the right. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com. Use --scope user to make it available across all projects.
2

Verify the connection

Start a Claude Code session and type /mcp to list connected servers. You should see chi-square-test-engine-mcp with a green status indicator.
3

Start using tools

Ask Claude Code something like "Check my latest Chi-Square Test Engine transactions." It will automatically discover and invoke the available Chi-Square Test Engine tools.

Terminal

claude mcp add --transport http chi-square-test-engine-mcp https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

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Prerequisites

Claude Code CLI installed
Active Vinkius subscription with a valid endpoint token

1

Open the config file

Create or edit .mcp.json in your project root for project-level scope, or ~/.claude.json for user-level scope.
2

Add the Chi-Square Test Engine MCP

Paste the JSON snippet shown on the right into the mcpServers object. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Restart Claude Code

Start a new Claude Code session. Type /mcp to confirm the server is connected. The tools will be automatically available in your conversation.

.mcp.json

{
  "mcpServers": {
    "chi-square-test-engine-mcp": {
      "type": "url",
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

Get your connection token →

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Common questions about Chi-Square Test Engine MCP in Claude Code

First, connect Claude Code to the server using the `claude mcp add` command. Then, in your script, you can pipe a JSON contingency table to `claude code --tool calculate_chi_square`. The result is printed to stdout for you to use.

It gives you a dead-simple, reliable way to run statistical tests in any automated, headless environment. You don't have to install and manage Python/R environments in your Docker containers or CI runners just for one test.

Yes. You run `claude mcp add --transport http chi-square-test -- `, replacing `` with the server endpoint. You can then check that it's connected with `claude mcp list`.

This server is a managed, deterministic service. You don't have to worry about maintaining the code, its dependencies, or the environment it runs in. It provides a standard, reliable calculation endpoint for any script or pipeline.

It's processed and immediately purged. Your contingency table is sent to a stateless, ephemeral instance for the calculation. The server retains no information about your request after sending the chi-squared value and p-value back to you.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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