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

Q: What kind of data does Zeabur (PaaS Deployment) touch when Vercel AI SDK sends emails?

The server touches email content and requires a ZEABUREMAILTOKEN for authentication. It processes the actual text payload you send.

Q: How do I handle multiple MCP Server calls with Zeabur (PaaS Deployment) and Vercel AI SDK?

You call mcpClient.tools() to get all available tools, then pass that list into your generation function. You always remember to call mcpClient.close() when you're done.

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Connect Zeabur (PaaS Deployment) MCP to Vercel AI SDK

Create your Vinkius account to connect Zeabur (PaaS Deployment) to Vercel AI SDK 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 Zeabur (PaaS Deployment) with Vercel AI SDK

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Vercel AI SDK: Deployments and State Management

Use `create_upload_stage` to set up an upload stage for pre-packaged apps. This gets your deployment assets ready so they can be processed by the MCP Server. You can then call `deploy_template` to roll out a service using YAML specifications, or use `prepare_deployment` after uploading files.

Vercel AI SDK: Container Interaction and Logging

Need to interact with an active container? Run commands inside it using `execute_command`, or grab specific data with `download_file`. When things go wrong, `get_build_logs` lets you retrieve the build logs for debugging.

Vercel AI SDK: Sending Transactional Communications

The MCP Server handles all your email needs. Send a single message using `send_email`, or blast out personalized messages with `send_batch_emails`. You can even schedule outreach for later via `schedule_email`.

Setup guide

Set up Zeabur (PaaS Deployment) MCP in Vercel AI SDK

Prerequisites

Node.js 18+ and a TypeScript project
ai + @modelcontextprotocol/sdk packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies

Run npm install ai @modelcontextprotocol/sdk plus your preferred model provider (e.g. @ai-sdk/openai).
2

Create the Streamable HTTP transport

Use StreamableHTTPClientTransport with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and use tools

Call mcpClient.tools() to auto-discover all Zeabur (PaaS Deployment) tools. Pass them directly to generateText() or streamText() — no manual schema definitions needed.
4

Works with any model provider

Swap openai("gpt-4o") for any AI SDK provider — Anthropic, Google, Mistral. The MCP tools work identically across all supported models.

index.ts

import { experimental_createMCPClient as createMCPClient } from "ai";
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

const transport = new StreamableHTTPClientTransport(
  new URL("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
);

const mcpClient = await createMCPClient({ transport });
const tools = await mcpClient.tools();

const { text } = await generateText({
  model: openai("gpt-4o"),
  tools,
  prompt: "List recent Zeabur (PaaS Deployment) transactions",
});

console.log(text);
await mcpClient.close();

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Common questions about Zeabur (PaaS Deployment) MCP in Vercel AI SDK

The MCP Server exposes tools directly to your TypeScript client. This means your user-facing product sees the deployment actions happen live, without waiting for a loading spinner.

Yep. The setup supports Edge Function compatibility. You can call the MCP tools directly from your edge code, keeping everything fast and local.

Absolutely. The MCP Server manages container operations like `deploy_template` and `execute_command`, giving you direct control over the lifecycle of your services.

The server touches email content and requires a `ZEABUR_EMAIL_TOKEN` for authentication. It processes the actual text payload you send.

You call `mcpClient.tools()` to get all available tools, then pass that list into your generation function. You always remember to call `mcpClient.close()` when you're done.

Use it with your favorite AI tools

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