How to Use the DataRobot MCP in OpenAI Agents SDK

Q: How do I fetch DataRobot project status in OpenAI Agents SDK?

You call the getproject tool within your agent definition. It returns the current state of your ML project as structured JSON. Your agent then uses that data to decide the next step.

Q: Can I list all DataRobot models in OpenAI Agents SDK?

You can call listmodels to pull the full register into your agent's memory. This lets your code map model versions to specific deployment tasks.

Plug DataRobot into your OpenAI Agents SDK pipeline to track production model deployments and dataset health without leaving your code.

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Connect DataRobot MCP to OpenAI Agents SDK

Create your Vinkius account to connect DataRobot to OpenAI Agents SDK 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 DataRobot with OpenAI Agents SDK

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Monitor model performance with OpenAI Agents SDK

Your agent pulls real-time status from DataRobot using `list_deployments`. It checks if your production endpoints are healthy or throwing errors. This MCP Server provides the exact feedback loop your agent needs to trigger alerts. Your code handles the logic while the server handles the DataRobot API calls.

Audit ML datasets through your agent

Use `list_datasets` to feed raw metadata directly into your agent's context. It identifies which files are ready for training runs. Your OpenAI Agents SDK system keeps the data pipeline clean. You stop guessing which files exist and start querying the registry directly.

Manage projects programmatically

The `get_project` tool lets your agent fetch specific configuration details. You map project IDs to active tasks without manual lookups. You build safer systems because your agent knows exactly what it's working on. It queries the DataRobot environment before making decisions.

Setup guide

Set up DataRobot MCP in OpenAI Agents SDK

Prerequisites

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

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all DataRobot tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives DataRobot tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate DataRobot tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="DataRobot Agent",
            instructions="You have access to DataRobot tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about DataRobot MCP in OpenAI Agents SDK

You call the `get_project` tool within your agent definition. It returns the current state of your ML project as structured JSON. Your agent then uses that data to decide the next step.

Yes. Every response from the server is parsed and validated before your agent acts on it. This keeps your production environment stable when querying model metadata.

Data flows through a secure, ephemeral tunnel managed by Vinkius. Only the specific model metadata you request is ever exposed to your agent. Your raw training data never leaves your infrastructure.

You can call `list_models` to pull the full register into your agent's memory. This lets your code map model versions to specific deployment tasks.

The server passes standard HTTP status codes through the tool response. Your agent logic catches these failures immediately, preventing execution of stale code.

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