How do I get my Trigger.dev API key?

Log in to your Trigger.dev dashboard at **cloud.trigger.dev**. Open your project, then find the **API Keys** section in the project settings. Copy your **Secret API Key** (it starts with `tr_dev_` for development or `tr_prod_` for production). Paste it into the configuration field below. For Personal Access Tokens (used for admin operations), go to **Profile → Personal Access Tokens** tab instead.

Can my AI agent tell me why a background job failed in production?

Yes. Ask your agent to list failed runs and it returns the task name, error message, stack trace, execution duration, and retry count for each failure. You can then drill into a specific run to see the exact input payload and which step failed — cutting your debugging time from minutes to seconds.

What if I'm on-call and need to check job health at 2 AM?

Just ask your AI agent 'Are there any failed jobs in production?' and get an instant triage report — number of failures, which tasks are affected, when they started failing, and whether retries resolved them. No need to open a browser, log in, or navigate dashboards in the middle of the night.

Does it support multiple environments like dev, staging, and production?

Yes. Each API key is scoped to a specific environment (dev or prod), just like in Trigger.dev itself. You can configure separate integrations for each environment, or switch between them by updating the API key — giving you full control over which environment your AI agent queries.

Trigger.dev MCP Server for Pydantic AI 8 tools — connect in under 2 minutes

Name: Trigger.dev
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 8 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Trigger.dev through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Trigger.dev "
            "(8 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Trigger.dev?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Trigger.dev MCP Server

Connect Trigger.dev to your AI agent and manage your background job infrastructure conversationally.

Pydantic AI validates every Trigger.dev tool response against typed schemas, catching data inconsistencies at build time. Connect 8 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Job Monitoring — List active, completed, and failed task runs with execution times, statuses, and error details.
Project Overview — Query projects, environments, and their associated job definitions.
Run Inspection — Drill into individual runs to view payloads, outputs, logs, and retry history.
Environment Management — Switch between dev, staging, and production environments to inspect runs across your deployment pipeline.

The Trigger.dev MCP Server exposes 8 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Trigger.dev to Pydantic AI via MCP

Follow these steps to integrate the Trigger.dev MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 8 tools from Trigger.dev with type-safe schemas

Why Use Pydantic AI with the Trigger.dev MCP Server

Pydantic AI provides unique advantages when paired with Trigger.dev through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Trigger.dev integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Trigger.dev connection logic from agent behavior for testable, maintainable code

Trigger.dev + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Trigger.dev MCP Server delivers measurable value.

Type-safe data pipelines: query Trigger.dev with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Trigger.dev tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Trigger.dev and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Trigger.dev responses and write comprehensive agent tests

Trigger.dev MCP Tools for Pydantic AI (8)

These 8 tools become available when you connect Trigger.dev to Pydantic AI via MCP:

cancel_run

Cancel a running task

get_run

Get run details

list_environments

List deployment environments

list_projects

List all projects

list_runs

List task runs

list_schedules

List cron schedules

replay_run

Replay a completed task

trigger_task

Trigger a background task

Example Prompts for Trigger.dev in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Trigger.dev immediately.

"Are there any failed background jobs in production?"

"Show me the details of the last 'process-webhook' run."

"How many jobs ran successfully today?"

Troubleshooting Trigger.dev MCP Server with Pydantic AI

Common issues when connecting Trigger.dev to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Trigger.dev + Pydantic AI FAQ

Common questions about integrating Trigger.dev MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Trigger.dev MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.