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

Q: How do I configure the K-Fold Split Engine with Claude Code?

Run claude mcp add --transport http k-fold-split-engine-mcp -- in your terminal. This registers the MCP server, making it immediately available for your command-line prompts.

Q: Can I use K-Fold Split Engine inside automated Claude Code scripts?

Yes, you can call the calculatekfold tool within your shell scripts or CI/CD pipelines. Claude Code executes the tool headlessly and pipes the index arrays directly into your training commands.

Q: Does the K-Fold Split Engine support multi-node splits in Claude Code?

The calculatekfold tool generates index partitions for a single dataset size. Claude Code can take these indices and distribute them across multiple nodes or containerized training jobs in your cluster.

Q: How do I verify that the MCP server is connected to my terminal agent?

Use the claude mcp list command in your terminal to view all active servers. You should see the split engine listed alongside its registered tool definitions.

Generate precise cross-validation index splits in your terminal with Claude Code and the K-Fold Split Engine.

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Connect K-Fold Split Engine MCP to Claude Code

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Setup K-Fold Split Engine with Claude Code

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Pipe Clean Validation Splits into Claude Code

The `calculate_kfold` tool outputs exact index arrays directly to your Claude Code session. Claude Code captures these indices, allowing you to pipe them into terminal-based python runners. No GUI is required to configure your validation loops using Claude Code. This MCP server lets you generate production-grade splits through simple Claude Code CLI prompts.

Build Headless Cross-Validation Pipelines

The `calculate_kfold` tool enables automated index partitioning inside headless CI/CD environments via our MCP server. Claude Code spins up the tool, extracts the train-test arrays, and formats them for your terminal-based scripts. You can trigger these splits inside Claude Code sessions running in GitHub Actions or Docker containers. The tool ensures your automated terminal pipelines use mathematically sound validation boundaries every time.

Script Leak-Proof Training Runs

The `calculate_kfold` tool calculates non-overlapping index limits to protect your terminal training runs from retrospective bias. Claude Code reads these limits and writes them directly into your shell execution environment. Your models train only on the designated fold indices provided to Claude Code. This Claude Code setup prevents lookahead issues, giving you reliable validation scores in your terminal output.

Setup guide

Set up K-Fold Split 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 k-fold-split-engine-mcp with a green status indicator.
3

Start using tools

Ask Claude Code something like "Check my latest K-Fold Split Engine transactions." It will automatically discover and invoke the available K-Fold Split Engine tools.

Terminal

claude mcp add --transport http k-fold-split-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 K-Fold Split 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": {
    "k-fold-split-engine-mcp": {
      "type": "url",
      "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 Code

Run `claude mcp add --transport http k-fold-split-engine-mcp -- ` in your terminal. This registers the MCP server, making it immediately available for your command-line prompts.

Yes, you can call the `calculate_kfold` tool within your shell scripts or CI/CD pipelines. Claude Code executes the tool headlessly and pipes the index arrays directly into your training commands.

The `calculate_kfold` tool generates index partitions for a single dataset size. Claude Code can take these indices and distribute them across multiple nodes or containerized training jobs in your cluster.

Use the `claude mcp list` command in your terminal to view all active servers. You should see the split engine listed alongside its registered tool definitions.

No, your dataset indices and split configurations are processed entirely inside an isolated sandbox. The tool never uploads your raw data or index layouts, ensuring absolute privacy for your machine learning terminal workflows.

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