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

Q: How do I check job status with Trigger.dev (Background Tasks & Jobs) and OpenAI Agents SDK?

Just use listruns to get a quick overview of all runs in the environment. For detailed logs on a single task, call getrun(runid) directly from your agent code.

Q: Can I schedule tasks using Trigger.dev (Background Tasks & Jobs) and OpenAI Agents SDK?

Absolutely. The createschedule tool lets you define an imperative cron job right in your agent's workflow, making it self-sufficient. You can then list all active schedules with listschedules.

Q: What if I need to stop a running task using Trigger.dev (Background Tasks & Jobs) and OpenAI Agents SDK?

You'll use the cancelrun tool. This lets your agent send a signal that halts an in-progress job immediately, which is crucial for maintaining stability in a production environment.

Q: How do I handle multiple tasks at once with Trigger.dev (Background Tasks & Jobs) and OpenAI Agents SDK?

The batchtriggertasks function lets your agent trigger dozens of related jobs in one call. If you need to check the outcome, use getbatchresults after the initial trigger.

Keep your production agents running smoothly. Manage background tasks and jobs using the OpenAI Agents SDK.

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Connect Trigger.dev (Background Tasks & Jobs) MCP to OpenAI Agents SDK

Create your Vinkius account to connect Trigger.dev (Background Tasks & Jobs) to OpenAI Agents 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 Trigger.dev (Background Tasks & Jobs) with OpenAI Agents SDK

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Monitor Background Job Status

Need to know if a scheduled task actually ran? You can check job status with `list_runs` or retrieve details for one specific execution using `get_run`. This gives your production agent visibility into the entire run history. If you need deep metrics, call `get_batch_results`. It pulls all results from a batch of tasks so your agent doesn't have to poll multiple endpoints just to confirm success.

Set Up Scheduled Jobs

Don't want to manually trigger every time? Use `create_schedule` to set up imperative cron-style jobs. This lets your agent automatically manage recurring tasks without needing external scheduling services. Want to tweak the job parameters before it runs? You can list current schedules with `list_schedules` and create new ones that fit right into your existing deployment architecture.

Manage Environment Settings

Your agent needs specific API keys or configuration flags to run. The toolset lets you manage these variables directly via functions like `create_env_var` and `update_env_var`. This means your deployment can dynamically change settings without code changes. If a setting gets stale, you can list all current environment variables using `list_env_vars`, ensuring that every action taken by the agent uses fresh data.

Setup guide

Set up Trigger.dev (Background Tasks & Jobs) MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Trigger.dev (Background Tasks & Jobs) tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Trigger.dev (Background Tasks & Jobs) tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Trigger.dev (Background Tasks & Jobs) tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Trigger.dev (Background Tasks & Jobs) Agent",
            instructions="You have access to Trigger.dev (Background Tasks & Jobs) tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Trigger.dev (Background Tasks & Jobs) MCP in OpenAI Agents SDK

Just use `list_runs` to get a quick overview of all runs in the environment. For detailed logs on a single task, call `get_run(run_id)` directly from your agent code.

Absolutely. The `create_schedule` tool lets you define an imperative cron job right in your agent's workflow, making it self-sufficient. You can then list all active schedules with `list_schedules`.

You'll use the `cancel_run` tool. This lets your agent send a signal that halts an in-progress job immediately, which is crucial for maintaining stability in a production environment.

The `batch_trigger_tasks` function lets your agent trigger dozens of related jobs in one call. If you need to check the outcome, use `get_batch_results` after the initial trigger.

It primarily manages job metadata: run IDs, environment variable names and values, and schedule definitions.

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