How to Use the Trigger.dev (Background Tasks & Jobs) MCP in Vercel AI SDK

Q: How do I check job status using Trigger.dev (Background Tasks & Jobs) with Vercel AI SDK?

You use getrun to get a specific run's details, which is ideal for streaming the current state directly into your UI. Since the Vercel AI SDK handles real-time data flow, you'll see updates instantly as they happen.

Q: Can I schedule tasks using Trigger.dev (Background Tasks & Jobs) with Vercel AI SDK?

Yep. You can set up recurring jobs by calling createschedule via the MCP Server. Your agent handles this call, and you'll see the new schedule listed when you check it with listschedules.

Q: Does Trigger.dev (Background Tasks & Jobs) support batch processing for Vercel AI SDK?

Absolutely. The createbatch tool lets your agent group multiple tasks together. After the jobs run, you can get a comprehensive summary of all outcomes using getbatchresults.

Q: What kind of data does this MCP Server touch?

It manages task identifiers, environment variables (createenvvar, deleteenvvar), and run metadata. Basically, it handles all the operational status data for your background jobs.

Q: How do I cancel a job using Trigger.dev (Background Tasks & Jobs) with Vercel AI SDK?

You just need to call cancelrun and pass the run ID. The MCP Server handles stopping the process, giving your agent immediate feedback that the cancellation was successful.

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Setup Trigger.dev (Background Tasks & Jobs) with Vercel AI SDK

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Monitor Tasks as They Happen

When your agent calls the MCP Server, it doesn't just run a job; it streams the status. You can trigger tasks using `trigger_task` and then stream the results from `get_run`. This means users see the job progress in real time—no loading spinner required. This live feedback loop is critical for user experience. The Vercel AI SDK lets your client call tools like `list_runs` and present that data immediately, making background processing feel instant.

Schedule Jobs Directly from the UI

Need a recurring job? Your agent can now create scheduled tasks using `create_schedule`. This tool lets you set up cron-like jobs without leaving your frontend. You simply call `list_schedules` to check what's already running. It’s pure, real-time control. Setting this up with the Vercel AI SDK means users can manage system processes—from creating environment variables via `create_env_var` to deleting them with `delete_env_var`—all while watching the changes happen on screen.

Batch Processing in One Go

The MCP Server supports bulk operations that are perfect for batch processing. You can use `create_batch` to group multiple actions, and then later retrieve all results using `get_batch_results`. It keeps your data clean and manageable. This capability lets you write a single tool call in the Vercel AI SDK that handles complex workflows. Instead of calling ten separate APIs, you trigger one batch job, simplifying both the code and the user experience.

Setup guide

Set up Trigger.dev (Background Tasks & Jobs) 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 Trigger.dev (Background Tasks & Jobs) 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 Trigger.dev (Background Tasks & Jobs) transactions",
});

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

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Common questions about Trigger.dev (Background Tasks & Jobs) MCP in Vercel AI SDK

You use `get_run` to get a specific run's details, which is ideal for streaming the current state directly into your UI. Since the Vercel AI SDK handles real-time data flow, you'll see updates instantly as they happen.

Yep. You can set up recurring jobs by calling `create_schedule` via the MCP Server. Your agent handles this call, and you'll see the new schedule listed when you check it with `list_schedules`.

Absolutely. The `create_batch` tool lets your agent group multiple tasks together. After the jobs run, you can get a comprehensive summary of all outcomes using `get_batch_results`.

It manages task identifiers, environment variables (`create_env_var`, `delete_env_var`), and run metadata. Basically, it handles all the operational status data for your background jobs.

You just need to call `cancel_run` and pass the run ID. The MCP Server handles stopping the process, giving your agent immediate feedback that the cancellation was successful.

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