How to Use the DataRobot MCP in Google ADK

Q: Will Google ADK agents see my DataRobot model list?

By executing listmodels, your agent gets a live feed of all models in your account. The tool response is formatted for easy integration with your Gemini agent's prompt context.

Connect DataRobot to Google ADK to keep your Gemini-powered agents updated on ML project status and deployment logs.

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Connect DataRobot MCP to Google ADK

Create your Vinkius account to connect DataRobot to Google ADK 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 Google ADK

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Track model deployments via Google ADK

Your agent runs `list_deployments` to see what is currently serving traffic. It maps these deployments to your existing Google Cloud infrastructure. Gemini models ingest this metadata to reason about production health. You get a clear picture of your ML ecosystem without manual reporting.

Search DataRobot datasets with Google ADK

The `list_datasets` tool gives your agent a complete inventory of your training data. It identifies which datasets require attention or updates. You avoid stale data issues by letting your agent verify registry contents. It pulls the latest metadata into your Google ADK workflow.

Access project metadata in Google ADK

Call `get_project` to retrieve specific configuration details for any ML task. Your agent uses this information to align its logic with active project requirements. This bridge between DataRobot and your agent ensures constant parity. You stop jumping between tabs to check project status.

Setup guide

Set up DataRobot MCP in Google ADK

Prerequisites

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

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with DataRobot tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="DataRobot_agent",
    model="gemini-2.0-flash",
    instruction="You have access to DataRobot tools via MCP.",
    tools=mcp_tools,
)

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Common questions about DataRobot MCP in Google ADK

You include the MCP toolset in your agent configuration. Once connected, your Google ADK agent calls tools like `list_projects` to gather information.

By executing `list_models`, your agent gets a live feed of all models in your account. The tool response is formatted for easy integration with your Gemini agent's prompt context.

All communication is encrypted and scoped to your unique endpoint token. This MCP Server only touches metadata like project names and deployment status, never your sensitive underlying training sets.

Yes, once you pass the server as a toolset to your agent. It handles the transport layer so you can focus on building your agent's logic.

The server is lightweight and built for rapid context lookups. Gemini models receive the tool output instantly, keeping your reasoning chain fast.

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