How to Use the Northflank (Developer Cloud & Orchestration) MCP in Google ADK

Q: Can the agent manage multiple microservices simultaneously?

Yes, the agent calls listservices to map out all active containers. It can then cycle specific replicas using restartservice based on performance metrics.

Run enterprise deployments and control your Northflank infrastructure using the Google ADK and Gemini long-context reasoning.

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Connect Northflank (Developer Cloud & Orchestration) MCP to Google ADK

Create your Vinkius account to connect Northflank (Developer Cloud & Orchestration) 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 Northflank (Developer Cloud & Orchestration) with Google ADK

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Long-context analysis via Google ADK

The `list_services` tool returns the complete list of running container instances in your project via this MCP Server. Because the Google ADK uses Gemini's massive context window, you can feed months of deployment logs and service metrics into a single prompt. The agent reads the service structure using `get_service` and compares it against your BigQuery historical performance data. This comparison helps the agent identify under-provisioned resources before they cause downtime.

Manage environments using Google ADK

The `create_project` tool provisions an isolated space and assigns cloud region routing parameters. Your Gemini-powered agent triggers this tool to set up staging environments for pull requests. Once testing finishes, the agent invokes `delete_project` to clean up the resources. This automated lifecycle management keeps your cloud spend under control without manual intervention.

Manage batch jobs and builds

The `list_jobs` tool retrieves active cron and batch jobs running in your cloud space through the MCP framework. Your agent monitors these background processes and correlates execution times with Vertex AI model training pipelines. If a job hangs, the agent uses `trigger_build` to push a hotfix or restarts the underlying service. This keeps your data pipelines moving without human operators watching the terminal.

Setup guide

Set up Northflank (Developer Cloud & Orchestration) 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 Northflank (Developer Cloud & Orchestration) 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="Northflank (Developer Cloud & Orchestration)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Northflank (Developer Cloud & Orchestration) tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Northflank (Developer Cloud & Orchestration) MCP in Google ADK

You initialize the McpToolset using the streamable HTTP transport parameters pointing to your Vinkius MCP endpoint. The SDK handles tool discovery automatically, exposing tools like `list_services` to Gemini.

Yes, you can pass a list of allowed tool names to the toolset configuration. This prevents the agent from running destructive actions like `delete_project` while still allowing it to call `get_service`.

The agent queries your database schemas in BigQuery and uses that schema context to configure environment variables. It then updates your services by running `list_secrets` and applying the new configurations.

Yes, the agent calls `list_services` to map out all active containers. It can then cycle specific replicas using `restart_service` based on performance metrics.

This MCP Server handles environment variables, build configurations, and database credentials. All data is transmitted over encrypted TLS channels directly to your agent, bypassing persistent storage to keep your infrastructure credentials secure.

Use it with your favorite AI tools

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