How to Use the Normality Test Engine MCP in Cursor

Inject deterministic Jarque-Bera test results straight into your codebase using Cursor and this MCP Server.

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Connect Normality Test Engine MCP to Cursor

Create your Vinkius account to connect Normality Test Engine to Cursor and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Normality Test Engine with Cursor

Ask AI about this MCP

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Claude

Perplexity

Stop Guessing Distribution Shapes

The `test_normality` tool calculates exact skewness and kurtosis metrics for the data structures you are actively coding. When you are writing a data pipeline in Cursor, you need to know if the incoming arrays follow a normal distribution. You ask the agent, and it runs the math right there. Instead of writing a temporary script to check your data, the agent handles it mid-flow. It passes your numeric arrays to the MCP Server, gets the precise Jarque-Bera results back, and uses that reality to write the next block of parametric testing code. No mocked responses.

True Math in Agent Mode

Invoking `test_normality` guarantees your AI client relies on a deterministic backend rather than its own flawed arithmetic logic. Large language models fail miserably at calculating kurtosis on raw arrays. This integration shifts the burden to dedicated mathematical functions. Cursor reads the exact statistical output from the tool and injects those metrics into your editor context. If the test rejects the null hypothesis, the agent knows to write non-parametric fallback code. You get statistically sound logic generated on the fly.

Project-Scoped MCP Server Setup

Setting up `test_normality` takes ten seconds by adding a simple JSON file to your project root. You define the command and arguments in your local configuration file. The editor automatically picks up the settings and registers the capability in your chat panel. Keeping the setup scoped to the project means your data science repository has the exact tools it needs without cluttering your global environment. Anyone who pulls the repo and opens it in this editor instantly gets the same statistical validation capabilities.

Setup guide

Set up Normality Test Engine MCP in Cursor

Prerequisites

Cursor installed (macOS, Windows, or Linux)
Active Vinkius subscription with a valid endpoint token

1

Open MCP Settings

Go to Cursor Settings → MCP or open the Command Palette (Cmd+Shift+P / Ctrl+Shift+P) and search for "MCP: Add Server".
2

Add the Normality Test Engine MCP

Cursor will create or open .cursor/mcp.json in your project root. Paste the JSON snippet on the right. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Enable Agent mode

Open Composer (Cmd+I / Ctrl+I) and switch to Agent mode using the dropdown at the top. MCP tools are only available in Agent mode.
4

Verify the connection

Ask Cursor something like "List my recent Normality Test Engine transactions." If the MCP tools are loaded correctly, Cursor will call the Normality Test Engine tools automatically. You can also check Settings → MCP for a green status indicator.

.cursor/mcp.json

{
  "mcpServers": {
    "normality-test-engine-mcp": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

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Common questions about Normality Test Engine MCP in Cursor

Create an mcp.json file in your project directory. Add the server command, switch your chat to Agent mode, and the tool will be ready to call.

Speed and context. The agent runs the test on your live data arrays and immediately uses the real results to write the correct statistical models in your active file.

Up to a point. Very large samples will trigger rejection on tiny deviations due to the Central Limit Theorem. Keep your inputs moderate for the most accurate methodological guidance.

You get garbage statistical results. LLMs cannot calculate Jarque-Bera metrics accurately. This MCP Server forces the agent to use a deterministic backend instead.

Your flat lists of numeric values never leave your local environment. The execution happens as a secure, local subprocess, meaning your proprietary experimental data is completely shielded from external network requests.

Use it with your favorite AI tools

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

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