How to Use the Trigger.dev MCP in Cursor

Q: How does Trigger.dev help with development context in Cursor?

The MCP Server allows the agent to call tools like listprojects and listenvironments. This means when you're writing code, the AI knows exactly what projects and deployment targets exist without needing external documentation.

Q: What if I need to test a task run in Cursor?

You can use triggertask(project, task) to start a background job. The resulting ID is live data that the AI can inject directly into your function or class structure for immediate testing.

Q: Is this MCP Server useful for CI/CD debugging in Cursor?

Absolutely. By calling getrun(runid) and replayrun(runid), the agent gets real-time execution context, letting you debug complex pipeline failures right where you're writing code.

Q: Do I need to configure anything for Trigger.dev in Cursor?

You create a .cursor/mcp.json file in your project root or globally. You add the server details there and make sure Agent mode is enabled in chat.

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Connect Trigger.dev MCP to Cursor

Create your Vinkius account to connect Trigger.dev to Cursor 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 with Cursor

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Discover project scope using Cursor

Starting a new feature? Use `list_projects` to see every available project in your account. You can then narrow down the context by calling `list_environments`, which shows all deployment targets for that specific project. This ability means the AI generates code that references real, known environments. It's not just stubbing out names; it knows what infrastructure you're dealing with.

Run and debug tasks directly in Cursor

If your code hits a background job issue, the `get_run(run_id)` tool lets the AI fetch live run details. This means when it suggests debugging steps or updated logic, it's based on real API responses. You can also force execution via `trigger_task(project, task)`. The resulting task ID can then be immediately used in your code as a variable.

Audit and fix workflows with Cursor

When testing complex logic, you need to know what happened previously. Use `list_runs` to see the history of executions. Then, if the bug was intermittent, you can use `replay_run(run_id)` to re-execute the task inside your coding session. This capability lets the AI generate code that handles failure states based on real historical data.

Setup guide

Set up Trigger.dev MCP in Cursor

Prerequisites

Cursor installed (macOS, Windows, or Linux)
Active Vinkius subscription with a valid endpoint token

1

Open MCP Settings

Go to Cursor Settings → MCP or open the Command Palette (Cmd+Shift+P / Ctrl+Shift+P) and search for "MCP: Add Server".
2

Add the Trigger.dev MCP

Cursor will create or open .cursor/mcp.json in your project root. Paste the JSON snippet on the right. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Enable Agent mode

Open Composer (Cmd+I / Ctrl+I) and switch to Agent mode using the dropdown at the top. MCP tools are only available in Agent mode.
4

Verify the connection

Ask Cursor something like "List my recent Trigger.dev transactions." If the MCP tools are loaded correctly, Cursor will call the Trigger.dev tools automatically. You can also check Settings → MCP for a green status indicator.

.cursor/mcp.json

{
  "mcpServers": {
    "triggerdev-mcp": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

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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 MCP in Cursor

The MCP Server allows the agent to call tools like `list_projects` and `list_environments`. This means when you're writing code, the AI knows exactly what projects and deployment targets exist without needing external documentation.

You can use `trigger_task(project, task)` to start a background job. The resulting ID is live data that the AI can inject directly into your function or class structure for immediate testing.

Absolutely. By calling `get_run(run_id)` and `replay_run(run_id)`, the agent gets real-time execution context, letting you debug complex pipeline failures right where you're writing code.

You create a `.cursor/mcp.json` file in your project root or globally. You add the server details there and make sure Agent mode is enabled in chat.

It deals with Project Metadata and Task Run IDs, giving you access to operational data about which projects exist and the historical execution status of tasks within those projects.

Use it with your favorite AI tools

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