How to Use the Zeplo (Queue & Background Job API) MCP in Vercel AI SDK

Q: If my user needs a new workspace, how is that managed using Zeplo (Queue & Background Job API) and Vercel AI SDK?

You use createtoken to generate a unique access token for the workspace. The agent passes this token value back to your client, which can then securely pass it to other tools like enqueuerequest. This manages user permissions cleanly.

Streamline Real-Time Data with Zeplo (Queue & Background Job API) for Vercel AI SDK.

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Connect Zeplo (Queue & Background Job API) MCP to Vercel AI SDK

Create your Vinkius account to connect Zeplo (Queue & Background Job API) 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 Zeplo (Queue & Background Job API) with Vercel AI SDK

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Manage Webhooks and Jobs via MCP Server

Your agent can check queue status and job history directly using `get_queue` or list past events with `list_queue_logs`. This lets your frontend show the user exactly where a background task stands, eliminating loading spinners. You'll never have to predict when a complex operation finishes. Use `enqueue_request` to start a job and then expose tools like `get_request` so the client can stream progress data into the UI as it happens.

Schedule Tasks for Vercel AI SDK

Need something to happen at a specific time? You can build new recurring triggers using `create_schedule`, and then manage them with `update_schedule`. This lets you schedule data refreshes or report generation, making your user-facing product reliable. It’s simple: the agent calls `list_schedules` to see what's already running, or uses `create_schedule` to set up a new job that runs independently of the current conversation.

Control and Clean Up MCP Server Resources

Sometimes you need to stop a job mid-stream. The agent can cancel any pending work using `cancel_request`, saving compute time and resources. Furthermore, if the structure changes, you have control over the environment by calling tools like `delete_queue` or `delete_schedule`. This level of resource management keeps your Edge Functions clean.

Setup guide

Set up Zeplo (Queue & Background Job API) 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 Zeplo (Queue & Background Job API) 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 Zeplo (Queue & Background Job API) transactions",
});

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

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Common questions about Zeplo (Queue & Background Job API) MCP in Vercel AI SDK

The MCP Server handles background processing. When you call `enqueue_request`, the job starts running outside of your client's request cycle. Your streaming UI can then poll tools like `get_request` to pull status updates, making the process feel instantaneous.

Absolutely. The MCP Server allows you to list and check various queues with `list_queues`. You can then use `get_queue` to monitor the metrics for any specific queue, ensuring your application handles multiple concurrent tasks.

You use `create_token` to generate a unique access token for the workspace. The agent passes this token value back to your client, which can then securely pass it to other tools like `enqueue_request`. This manages user permissions cleanly.

The server handles general HTTP requests and webhooks. While the specific payload type depends on your backend, the MCP Server structure ensures that the agent can reliably pass necessary metadata—like queue names or schedules—to execute the job.

This server primarily handles operational and scheduling data, including unique request IDs, queue names, schedule definitions, and workspace tokens. It doesn't process sensitive user input data directly.

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