How to Use the K-Fold Split Engine MCP in Claude

Q: How does K-Fold Split Engine prevent data leakage in Claude Desktop?

The K-Fold Split Engine uses calculatekfold to isolate training rows from test rows before Claude Desktop writes any training code. This local execution ensures no validation indices overlap, keeping your model metrics accurate inside the Claude Desktop interface.

Q: How do I configure the K-Fold Split Engine MCP Server in my Claude Desktop JSON file?

You add the K-Fold Split Engine to your claudedesktopconfig.json under the mcpServers key to make the calculatekfold tool available. Once saved, restart Claude Desktop to see the partition tools in your chat window.

Run the K-Fold Split Engine locally in Claude Desktop to partition machine learning training data without data leakage.

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Connect K-Fold Split Engine MCP to Claude Desktop

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Setup K-Fold Split Engine with Claude Desktop

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Prevent Claude Desktop data leakage with local splits

The `calculate_kfold` tool runs directly inside your Claude Desktop environment to generate mathematical indices for your training and testing partitions. Instead of letting Claude Desktop guess partition boundaries, the K-Fold Split Engine enforces strict statistical separation across your dataset. Your Claude Desktop client processes these K-Fold Split Engine index arrays locally through standard input/output streams. This local execution means your raw training data never leaves your machine while Claude Desktop structures the validation folds.

Feed exact validation folds to your Claude Desktop agent

The `calculate_kfold` tool calculates exact index sets so your local Claude Desktop client can write training scripts with zero partition overlap. The K-Fold Split Engine outputs raw integer arrays that map directly to your local pandas DataFrames within the Claude Desktop interface. You can ask the Claude Desktop app to build a cross-validation loop, and it will read these precise K-Fold Split Engine indices to write the execution code. This prevents the common issue where Claude Desktop writes synthetic split logic that secretly leaks future target values.

Connect this MCP Server to Claude Web for remote pipelines

The `calculate_kfold` tool integrates with your Claude Web account via custom remote connectors to partition datasets using the K-Fold Split Engine. You paste the remote endpoint into your browser settings to give the Claude Web agent direct access to the partition engine. This setup lets you run the MCP Server on a secure server while interacting with the K-Fold Split Engine through the claude.ai chat interface. The Claude Web client handles the UI rendering while the remote engine does the heavy partition math.

Setup guide

Set up K-Fold Split Engine MCP in Claude Web or Desktop

1

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
2

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL: https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.
3

Start a conversation

Open a new chat. The K-Fold Split Engine MCP tools are available immediately — no restart needed.

Endpoint URL

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

No configuration file needed — paste the URL directly in the Claude web interface.

Available on Free (1 connector), Pro, Max, Team, and Enterprise plans.

Prerequisites

Claude Desktop installed (macOS or Windows)
Active Vinkius subscription with a valid endpoint token

1

Open Claude Desktop Settings

Click the menu icon at the top-left corner, go to Settings → Developer → Edit Config. This opens claude_desktop_config.json in your default text editor.
2

Paste the K-Fold Split Engine MCP configuration

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

Restart Claude Desktop

Close and reopen the application. Claude needs a full restart to load new MCPs — refreshing a conversation is not enough.
4

Verify the connection

Open a new conversation. Click the 🔌 icon at the bottom of the message input. You should see tools listed under k-fold-split-engine-mcp.

json

{
  "mcpServers": {
    "k-fold-split-engine-mcp": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

Get your connection token →

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Common questions about K-Fold Split Engine MCP in Claude Desktop

The K-Fold Split Engine uses `calculate_kfold` to isolate training rows from test rows before Claude Desktop writes any training code. This local execution ensures no validation indices overlap, keeping your model metrics accurate inside the Claude Desktop interface.

Yes, the K-Fold Split Engine runs as a local subprocess through your Claude Desktop configuration. Your raw data stays on your local drive, and only the K-Fold Split Engine index generation instructions pass through the local MCP protocol to Claude Desktop.

You add the K-Fold Split Engine to your `claude_desktop_config.json` under the `mcpServers` key to make the `calculate_kfold` tool available. Once saved, restart Claude Desktop to see the partition tools in your chat window.

This version of the K-Fold Split Engine generates standard validation indices using `calculate_kfold` inside Claude Desktop. It splits tabular datasets sequentially or randomly based on your parameters, but does not support stratified class balancing in Claude Desktop yet.

The K-Fold Split Engine only processes index arrays on your local CPU. Claude Desktop reads these generated index lists to partition your local CSV or parquet files without exposing your tabular records to external APIs.

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