How to Use the Conductor (Netflix OSS) MCP in Google ADK

Q: How do I configure Conductor (Netflix OSS) inside a Google ADK agent?

Install the package, then initialize McpToolset with your Vinkius HTTP transport URL. Pass this toolset directly into the tools list when instantiating your LlmAgent.

Q: Can Gemini models use Conductor (Netflix OSS) to build new workflows in Google ADK?

Yes, they can. The agent leverages createworkflowdefinition to build new executions and validateworkflowdefinition to ensure the structure is correct before saving.

Q: Can I restrict which MCP Server tools are exposed to the agent to prevent accidental deletions?

Yes, you can use the optional toolnames filter during toolset initialization. This lets you exclude destructive tools like deleteworkflowdefinition or bulkterminate.

Control complex pipeline executions in Google ADK using Gemini long-context reasoning and MCP.

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Connect Conductor (Netflix OSS) MCP to Google ADK

Create your Vinkius account to connect Conductor (Netflix OSS) 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 Conductor (Netflix OSS) with Google ADK

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Enterprise workflow execution via Google ADK

This MCP server connects your Gemini-powered agent to your workflow engine. The agent triggers runs using `start_workflow` or `execute_workflow`, passing parameters extracted from your BigQuery datasets. Long-context models digest massive execution histories easily. The agent retrieves complete execution details with `get_workflow` and analyzes the entire run history to make smart routing decisions.

Queue management and task polling

This queue monitoring server pulls jobs and tracks worker loads. The agent uses `poll_task` and `poll_batch_tasks` to pull jobs directly from your queues, processing them using your Vertex AI pipelines. Monitoring queue health is simple. The agent calls `get_all_queues` or `get_queue_size` to track pending counts, adjusting worker scale dynamically based on current demand.

Granular task state manipulation

This task execution server modifies running job states directly. The agent modifies status using `update_task` or `update_task_by_ref` as soon as external processing finishes. If a task needs to be bypassed, the agent runs `skip_task` to keep the workflow moving. It can also run `requeue_tasks` to retry failed queue items without restarting the whole run.

Setup guide

Set up Conductor (Netflix OSS) 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 Conductor (Netflix OSS) 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="Conductor (Netflix OSS)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Conductor (Netflix OSS) tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Conductor (Netflix OSS) MCP in Google ADK

Install the package, then initialize `McpToolset` with your Vinkius HTTP transport URL. Pass this toolset directly into the tools list when instantiating your `LlmAgent`.

Yes, they can. The agent leverages `create_workflow_definition` to build new executions and `validate_workflow_definition` to ensure the structure is correct before saving.

The server serializes the JSON outputs cleanly. Because Gemini models handle large contexts, you can pass the entire output of `search_workflows_v2` directly to the model for analysis.

Yes, you can use the optional tool_names filter during toolset initialization. This lets you exclude destructive tools like `delete_workflow_definition` or `bulk_terminate`.

All task payloads and parameters pass through secure, encrypted memory. The Vinkius sandbox executes the adapter in isolation, ensuring no credentials or workflow metadata are stored on external disks.

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