How to Use the K-Fold Split Engine MCP in Mastra AI

Automate data partitioning in Mastra AI workflows using the K-Fold Split Engine for reliable, repeatable model testing.

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Connect K-Fold Split Engine MCP to Mastra AI

Create your Vinkius account to connect K-Fold Split Engine to Mastra AI 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 K-Fold Split Engine with Mastra AI

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Automate splits in Mastra AI

Drop the `calculate_kfold` tool into your agent workflows to handle dataset partitioning automatically. It manages the index generation so your agents can focus on the next step. If the process hits a snag, you can set up retries within your workflow definition. It keeps your pipeline moving without manual intervention.

Conditional branching with MCP Server

Use the output of this MCP Server to trigger different branches in your logic. If the split indices indicate a specific class imbalance, your agent can route to a re-sampling step. It makes your workflow logic tighter. You define the rules, and the tool provides the data to execute them.

Fail-safe data partitioning

Mastra AI handles the tool execution, while we ensure the indices are accurate. You can mandate human-in-the-loop approval before the split proceeds. This ensures your training sets are exactly what you expect. It is a reliable way to harden your data preparation phase.

Setup guide

Set up K-Fold Split Engine MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All K-Fold Split Engine tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "k-fold-split-engine-mcp-client",
  servers: {
    "k-fold-split-engine-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "K-Fold Split Engine Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to K-Fold Split Engine tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent K-Fold Split Engine transactions"
);
console.log(result.text);

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Common questions about K-Fold Split Engine MCP in Mastra AI

Import the client and pass the tools into your agent configuration. It detects the server connection automatically.

Yes. You can require a human to sign off on the split indices before the pipeline continues.

You can configure your workflow to retry the split if the tool execution fails. It integrates with standard backoff settings.

Every tool call runs in a dedicated sandbox. No data persists between your workflow steps.

The server treats your index requests as ephemeral. It only touches the structural metadata required for partitioning.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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