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

Q: How do I monitor job status using Trigger.dev (Background Tasks & Jobs) with CrewAI?

The dedicated monitor agent checks listruns and uses getrun to track the progress of an autonomous operation. This keeps the whole crew informed about task completion or failure.

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

Yes, you can set up recurring maintenance jobs by calling createschedule. This lets your autonomous operations run on a predictable, managed timeline without constant human supervision.

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

It does. You use the MCP Server to execute createbatch when several specialized agents need to work on a set of related data points simultaneously, gathering all results via getbatchresults.

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

You simply call cancelrun and provide the specific run ID. This is your immediate stop button for any autonomous operation that needs to be halted before it finishes.

Run Autonomous Operations with CrewAI (Background Tasks & Jobs) using Multi-Agent Coordination.

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Setup Trigger.dev (Background Tasks & Jobs) with CrewAI

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Perplexity

Coordinate Agent Actions via Task Triggers

When a team needs to act, the MCP Server provides the mechanism. One agent can trigger another's task using `trigger_task`, passing it specific parameters like an environment variable. This lets you sequence specialized roles into one operation. This is perfect for autonomous operations where Agent A researches data, and then passes that context to Agent B, which uses the MCP Server to execute a follow-up action.

Shared Memory Management

The ability to manage environment variables (`create_env_var`, `list_env_vars`) gives your crew shared memory. Agents don't have to re-read data; they just check the variable store for context. This makes the agents more cohesive and autonomous. They can pass sensitive keys or configuration parameters between themselves using these tools.

Monitoring Autonomous Operations

A monitor agent watches everything by listing past jobs with `list_runs`. If things go sideways, a moderator agent can step in and read the detailed results using `get_batch_results` to figure out where the team failed. If an operation needs to be redone, you use `replay_run` so the crew doesn't have to start from scratch.

Setup guide

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

Prerequisites

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

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Trigger.dev (Background Tasks & Jobs) tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Trigger.dev (Background Tasks & Jobs) Analyst",
    goal="Access and analyze Trigger.dev (Background Tasks & Jobs) data via MCP.",
    backstory="Expert analyst with direct Trigger.dev (Background Tasks & Jobs) access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Trigger.dev (Background Tasks & Jobs) transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Trigger.dev (Background Tasks & Jobs) Analyst",
        goal="Access and analyze Trigger.dev (Background Tasks & Jobs) data via MCP.",
        backstory="Expert analyst with direct Trigger.dev (Background Tasks & Jobs) access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Trigger.dev (Background Tasks & Jobs) transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Trigger.dev. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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

The dedicated monitor agent checks `list_runs` and uses `get_run` to track the progress of an autonomous operation. This keeps the whole crew informed about task completion or failure.

Yes, you can set up recurring maintenance jobs by calling `create_schedule`. This lets your autonomous operations run on a predictable, managed timeline without constant human supervision.

It does. You use the MCP Server to execute `create_batch` when several specialized agents need to work on a set of related data points simultaneously, gathering all results via `get_batch_results`.

It touches task identifiers, environment variables, and run metadata. These are the essential operational details that allow your specialized agents to pass context and track progress.

You simply call `cancel_run` and provide the specific run ID. This is your immediate stop button for any autonomous operation that needs to be halted before it finishes.

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