How to Use the Trigger.dev MCP in Mastra AI

Q: How do I start and monitor a background job using Trigger.dev and Mastra AI?

You first call triggertask to initiate the workflow run. Then, you track progress by calling listruns or getting granular status with getrun. This allows your agent to build conditional logic based on task completion.

Q: Can Mastra AI list all available deployment environments using Trigger.dev?

Yes, you just call listenvironments. This confirms the exact scope for any multi-step workflow, which is crucial for robust agent execution.

Q: What if a task fails during a complex Mastra AI workflow with Trigger.dev?

You can use cancelrun to halt the failing job immediately or check its history using getrun. This gives your agent enough context to decide whether an automatic retry is safe.

Q: How do I view scheduled tasks when building workflows with Trigger.dev and Mastra AI?

Use listschedules to see all cron job definitions in the system. This helps your agent map out where automated processes are running independently of any workflow.

Build resilient, self-healing workflows with Mastra AI and Trigger.dev's task management.

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Setup Trigger.dev with Mastra AI

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Execute and Track Background Jobs

Your agent triggers tasks using `trigger_task(project, job)`. If the workflow needs monitoring during execution, it can call `list_runs` to get an immediate overview of all current jobs. If a task completes successfully but you need proof, use `get_run(run_id)` to pull the full historical details. This helps Mastra AI build reliable multi-step processes.

Define and Verify Scope

Mastra AI uses `list_projects` and `list_environments` to confirm which scope is active for a workflow run. You're always protected from running code against the wrong environment. This ability ensures that when Mastra AI performs conditional branching, it's operating on the correct, validated data set.

Manage Failures and Retries

If a step fails, you can use `cancel_run(run_id)` to stop it manually. Otherwise, check its status with `get_run` before initiating the built-in Mastra AI retry logic. For critical steps, you might also use `replay_run(run_id)` to re-execute a task that failed due to temporary data issues.

Setup guide

Set up Trigger.dev MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Trigger.dev tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "triggerdev-mcp-client",
  servers: {
    "triggerdev-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Trigger.dev Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Trigger.dev tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Trigger.dev transactions"
);
console.log(result.text);

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Common questions about Trigger.dev MCP in Mastra AI

You first call `trigger_task` to initiate the workflow run. Then, you track progress by calling `list_runs` or getting granular status with `get_run`. This allows your agent to build conditional logic based on task completion.

Yes, you just call `list_environments`. This confirms the exact scope for any multi-step workflow, which is crucial for robust agent execution.

You can use `cancel_run` to halt the failing job immediately or check its history using `get_run`. This gives your agent enough context to decide whether an automatic retry is safe.

Use `list_schedules` to see all cron job definitions in the system. This helps your agent map out where automated processes are running independently of any workflow.

It manages operational run details, including project names, environment scopes, and the specific status/logs associated with a triggered background job.

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