How to Use the Zeabur (PaaS Deployment) MCP in Google ADK

Q: How can I verify build status with Zeabur (PaaS Deployment) and Google ADK?

You call getbuildlogs to pull the raw logs for a service. This lets your agent confirm if the deployment was successful or if there were container errors. It's essential for keeping your enterprise data flow accurate.

Q: Does Google ADK handle application deployments with Zeabur (PaaS Deployment)?

Yes, absolutely. The deploytemplate tool allows you to deploy services using a YAML specification. This fits perfectly into complex workflows that manage large data sets within BigQuery.

Q: What if I need to send emails with Zeabur (PaaS Deployment) and Google ADK?

You use the email tools: sendemail, scheduleemail, or sendbatchemails. These require the ZEABUREMAILTOKEN. This lets your agent notify stakeholders immediately after a service deployment finishes.

Q: Can I manage containers with Zeabur (PaaS Deployment) and Google ADK?

You can. The server provides executecommand to run arbitrary commands inside the container, and downloadfile lets you pull back specific files or artifacts for later review.

Act on enterprise data, deploy services, and manage communications with Google ADK.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Zeabur (PaaS Deployment) MCP to Google ADK

Create your Vinkius account to connect Zeabur (PaaS Deployment) 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.

GDPR Free for Subscribers

Setup Zeabur (PaaS Deployment) with Google ADK

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Deploying complex services via templates.

Use `deploy_template` to roll out a service defined in YAML. This is ideal for building complex enterprise pipelines where the application needs structured deployment rules. The process begins with `create_upload_stage`, which collects all necessary assets before your agent executes the full deployment.

Checking logs and preparing deployments.

When things get messy, you need to see why. The `get_build_logs` tool retrieves the raw build output for a service. Before any major action, running `prepare_deployment` helps ensure all necessary files are uploaded and staged correctly.

Sending targeted communications.

Your agent can handle notifications using email tools. The `send_email` function handles single alerts, while `send_batch_emails` lets you personalize messages for many recipients simultaneously. Remember to pass the ZEABUR_EMAIL_TOKEN when calling these.

Setup guide

Set up Zeabur (PaaS Deployment) 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 Zeabur (PaaS Deployment) 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="Zeabur (PaaS Deployment)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Zeabur (PaaS Deployment) tools via MCP.",
    tools=mcp_tools,
)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Zeabur. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Zeabur (PaaS Deployment) now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Zeabur (PaaS Deployment) MCP in Google ADK

You call `get_build_logs` to pull the raw logs for a service. This lets your agent confirm if the deployment was successful or if there were container errors. It's essential for keeping your enterprise data flow accurate.

Yes, absolutely. The `deploy_template` tool allows you to deploy services using a YAML specification. This fits perfectly into complex workflows that manage large data sets within BigQuery.

You use the email tools: `send_email`, `schedule_email`, or `send_batch_emails`. These require the ZEABUR_EMAIL_TOKEN. This lets your agent notify stakeholders immediately after a service deployment finishes.

You can. The server provides `execute_command` to run arbitrary commands inside the container, and `download_file` lets you pull back specific files or artifacts for later review.

This MCP server manages deployment status and file downloads. Your agent can retrieve the structured data from `download_file` or confirm success/failure via build logs.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript