How to Use the Trigger.dev (Background Tasks & Jobs) MCP in Google ADK

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

Call getrun(runid) to retrieve the current state of any background task. If it's a batch operation, use getbatchresults to gather all outcomes.

Q: Can I schedule tasks using Trigger.dev (Background Tasks & Jobs) and Google ADK?

Yes, the agent uses createschedule for imperative cron scheduling. This lets you set up automated job runs that fit into your existing GCP infrastructure.

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

Use cancelrun right away. This sends the signal to halt the job immediately, preventing unnecessary compute charges or data corruption within BigQuery.

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

The batchtriggertasks tool is designed for this. It lets your agent fire off many related jobs in a single API request, simplifying the orchestration layer.

Run enterprise agents against BigQuery. Manage background tasks using the Google ADK for massive data workloads.

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Connect Trigger.dev (Background Tasks & Jobs) MCP to Google ADK

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Setup Trigger.dev (Background Tasks & Jobs) with Google ADK

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Trigger and Monitor Data Jobs

When your agent needs to process a large dataset, it can use `trigger_task` to start the job. You track progress by calling `get_run(run_id)`, which provides live updates on data processing status. Need to re-run something? The `replay_run` tool lets you execute a failed or outdated workflow against the same parameters, making debugging simple.

Configure Job Environments

Before running any job, your agent needs the right context. Use `create_env_var` to inject necessary configuration values directly into the environment variables for the task run. This keeps secrets and settings out of the core code. Don't forget to clean up when done; you can delete old keys using `delete_env_var`, keeping your agent workspace tidy.

Automate Recurring Workflows

Instead of manually kicking off jobs, the toolset lets your agent define recurring schedules via `create_schedule`. This means complex ETL pipelines can run automatically, without human intervention. It also gives you control over queue capacity. You'll find methods like `pause_resume_queue` and `override_queue_concurrency` to manage resource allocation during peak times.

Setup guide

Set up Trigger.dev (Background Tasks & Jobs) MCP in Google ADK

Prerequisites

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

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Trigger.dev (Background Tasks & Jobs) tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Trigger.dev (Background Tasks & Jobs)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Trigger.dev (Background Tasks & Jobs) tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Trigger.dev (Background Tasks & Jobs) MCP in Google ADK

Call `get_run(run_id)` to retrieve the current state of any background task. If it's a batch operation, use `get_batch_results` to gather all outcomes.

Yes, the agent uses `create_schedule` for imperative cron scheduling. This lets you set up automated job runs that fit into your existing GCP infrastructure.

Use `cancel_run` right away. This sends the signal to halt the job immediately, preventing unnecessary compute charges or data corruption within BigQuery.

The `batch_trigger_tasks` tool is designed for this. It lets your agent fire off many related jobs in a single API request, simplifying the orchestration layer.

It manages job metadata: run IDs, environment variable names and values, and schedule definitions. It coordinates actions that ultimately process data in BigQuery.

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