How to Use the Zeabur (PaaS Deployment) MCP in AutoGen

Q: Can I make agents manage deployments using Zeabur (PaaS Deployment)?

Yes. You can pass tools like createuploadstage and preparedeployment to your AssistantAgent constructor. The agents will then debate the correct sequence for deployment.

Get consensus on deployments using AutoGen with the Zeabur PaaS Deployment MCP Server.

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Connect Zeabur (PaaS Deployment) MCP to AutoGen

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Setup Zeabur (PaaS Deployment) with AutoGen

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Perplexity

Debating Deployment Strategy

Multiple agents can debate the best deployment path. One agent might use `get_build_logs` to argue that the build failed, while a performance agent uses `execute_command` to test environment variables until consensus is reached on a fix.

Coordinating Email Campaigns

Agents can negotiate communication tasks. A marketing agent might decide they need personalized outreach and call `send_batch_emails`. A compliance agent then reviews the required email content, ensuring all necessary tokens are present for `schedule_email`.

Multi-Agent Workflow Orchestration

You can build complex pipelines where roles are assigned. For instance, a 'Deployment Agent' handles `deploy_template`, and a separate 'Reporting Agent' uses the status to trigger a report via `send_email`. The agents collaborate until all tasks converge.

Setup guide

Set up Zeabur (PaaS Deployment) MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Zeabur (PaaS Deployment) tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Zeabur (PaaS Deployment)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Zeabur (PaaS Deployment) data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Zeabur (PaaS Deployment)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Zeabur (PaaS Deployment) data")
    print(result.messages[-1].content)

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.

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Common questions about Zeabur (PaaS Deployment) MCP in AutoGen

The MCP Server provides discrete, callable actions that multiple agents can debate and agree upon. This lets your system achieve decisions—like deployment or communication—through consensus-driven reasoning.

The server deals primarily with deployment configuration data. This includes the YAML specifications and any necessary email tokens required for messaging tasks.

Yes. You can pass tools like `create_upload_stage` and `prepare_deployment` to your AssistantAgent constructor. The agents will then debate the correct sequence for deployment.

It supports any task that can be broken down into tool calls. Whether it's scheduling an email or running a container command, the multi-agent system coordinates the steps until completion.

The MCP Server touches deployment configuration data. This includes logs from `get_build_logs` and the initial parameters used to run a service container.

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