How to Use the Zeabur (PaaS Deployment) MCP in Pydantic AI

Q: How do I check build logs with Zeabur (PaaS Deployment) and Pydantic AI?

You call getbuildlogs to retrieve the raw log output. Because your agent uses Pydantic, it'll validate that the returned log structure is predictable—no unexpected fields or data types messing up your analysis.

Q: Can I manage deployments with Zeabur (PaaS Deployment) and Pydantic AI?

Yes. You use deploytemplate to deploy services based on a YAML specification. The type-safe nature of Pydantic means you trust the deployment status output, minimizing runtime errors.

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

You use sendemail, scheduleemail, or sendbatchemails. Your agent sends the request, and Pydantic validates the resulting status object. This ensures that even if the email service has weird output, your code won't break.

Q: How do I access files with Zeabur (PaaS Deployment) and Pydantic AI?

Use downloadfile to grab a file from the running container. This is useful after you run an operational command using executecommand. The data retrieved will be validated against your model, giving you confidence in its structure.

Ensure correctness: Manage deployments with Pydantic AI and structured outputs.

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Setup Zeabur (PaaS Deployment) with Pydantic AI

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Prepare staging areas for new deployments.

Before you roll out code, use `create_upload_stage` to set up a dedicated upload environment. Then, `prepare_deployment` finishes the pre-flight checks and stages all necessary files. Since Pydantic AI validates every output, your agent knows these staged assets are correctly formatted.

Executing commands reliably in containers.

The `execute_command` tool runs arbitrary shell commands inside the service container. If you're running diagnostics, you can use this and follow up with `download_file` to grab logs. Pydantic guarantees that any resulting data structure will conform to your expected schema.

Sending multiple personalized emails.

The `send_batch_emails` tool sends out alerts for many people at once, perfect for release notes. You can also use `send_email` for single confirmations. These email calls need the ZEABUR_EMAIL_TOKEN to work, and your agent relies on Pydantic's validation to ensure any returned status is trustworthy.

Setup guide

Set up Zeabur (PaaS Deployment) MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "zeabur-paas-deployment-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Zeabur (PaaS Deployment) tools.",
)

result = await agent.run("List recent Zeabur (PaaS Deployment) transactions")
print(result.output)

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Common questions about Zeabur (PaaS Deployment) MCP in Pydantic AI

You call `get_build_logs` to retrieve the raw log output. Because your agent uses Pydantic, it'll validate that the returned log structure is predictable—no unexpected fields or data types messing up your analysis.

Yes. You use `deploy_template` to deploy services based on a YAML specification. The type-safe nature of Pydantic means you trust the deployment status output, minimizing runtime errors.

You use `send_email`, `schedule_email`, or `send_batch_emails`. Your agent sends the request, and Pydantic validates the resulting status object. This ensures that even if the email service has weird output, your code won't break.

Use `download_file` to grab a file from the running container. This is useful after you run an operational command using `execute_command`. The data retrieved will be validated against your model, giving you confidence in its structure.

This MCP server deals with deployment status and file content. Your agent can use `download_file` to get structured artifacts, which are then validated by Pydantic against a defined schema.

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