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

Deploy a specialized agent crew in CrewAI to manage model validation using the K-Fold Split Engine.

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Connect K-Fold Split Engine MCP to CrewAI

Create your Vinkius account to connect K-Fold Split Engine to CrewAI 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 CrewAI

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Perplexity

Collaborative splits in CrewAI

Assign the `calculate_kfold` tool to your data-prep agent. It works alongside your research and analysis agents to maintain consistent validation indices. Shared memory allows your crew to pass these indices between tasks. It keeps your agents synchronized on the current data fold.

Modular MCP Server deployment

You can filter tools to ensure only the necessary agents have access to the split functionality. It keeps your crew focused on their specific roles. Adding it to your agent definition is straightforward. You just point the agent to the server URL.

Autonomous validation scaling

Your crew can run sequential splits as part of a larger autonomous loop. It handles the index generation while your monitor agent tracks the progress. This is how you build an operations team for your models. You set the goal, and the crew uses the tool to get the data.

Setup guide

Set up K-Fold Split Engine MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke K-Fold Split Engine tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="K-Fold Split Engine Analyst",
    goal="Access and analyze K-Fold Split Engine data via MCP.",
    backstory="Expert analyst with direct K-Fold Split Engine access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent K-Fold Split Engine transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="K-Fold Split Engine Analyst",
        goal="Access and analyze K-Fold Split Engine data via MCP.",
        backstory="Expert analyst with direct K-Fold Split Engine access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent K-Fold Split Engine transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

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Common questions about K-Fold Split Engine MCP in CrewAI

Include the server URL in your agent's MCP list. The tool will be available for that agent to execute immediately.

Yes. You can restrict the tool to specific agents, ensuring clean separation of duties within the crew.

Absolutely. Your agents can pass indices sequentially to downstream tasks for continuous model validation.

The server is built for headless operation. It provides consistent index outputs across long-running autonomous sessions.

We enforce strict isolation. Your training metadata is handled in a temporary environment that resets after every task.

Use it with your favorite AI tools

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