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

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

Use listruns to get a list of run IDs, then pass a specific ID into getrun. Since this MCP Server uses typed responses, you're guaranteed the data structure is correct.

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

You can use createschedule to set up recurring jobs. Because of Pydantic's validation, the resulting schedule object must adhere perfectly to your defined Python models.

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

The cancelrun function sends the termination signal. This action is exposed through the MCP Server, providing clear inputs and outputs that your agent validates at runtime.

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

The batchtriggertasks tool handles this by grouping calls into one request. The resulting batch results are then validated against your schema, preventing data corruption.

Build reliable agents with Pydantic AI. Control background tasks using the MCP Server for guaranteed, validated job execution.

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Connect Trigger.dev (Background Tasks & Jobs) MCP to Pydantic AI

Create your Vinkius account to connect Trigger.dev (Background Tasks & Jobs) to Pydantic AI 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 (Background Tasks & Jobs) with Pydantic AI

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Verify Job Status and History

You don't want silent failures; you need hard validation. The agent can check job status by listing runs with `list_runs` or retrieving deep details using `get_run`. If the API response structure is wrong, Pydantic fails loudly. To see results from a group task, call `get_batch_results`. This ensures that even complex output data conforms to your expected schemas.

Automate Job Scheduling

Define recurring tasks using `create_schedule` so the agent never forgets. The job schedule itself is treated as a strongly typed object, meaning you know exactly what fields are required. For immediate configuration changes, use `update_env_var`. If an environment variable doesn't match your defined schema, the whole process stops with a clear validation error.

Control Task Execution Flow

If a job goes sideways, you need to stop it. The `cancel_run` tool lets your agent terminate an active task safely. You can also pause or resume the entire job queue using `pause_resume_queue`. Want to run a specific set of tasks again? Use `replay_run`. Pydantic ensures that even when replaying, the inputs are correctly typed and structured.

Setup guide

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

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "triggerdev-background-tasks-jobs-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Trigger.dev (Background Tasks & Jobs) tools.",
)

result = await agent.run("List recent Trigger.dev (Background Tasks & Jobs) transactions")
print(result.output)

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

Use `list_runs` to get a list of run IDs, then pass a specific ID into `get_run`. Since this MCP Server uses typed responses, you're guaranteed the data structure is correct.

You can use `create_schedule` to set up recurring jobs. Because of Pydantic's validation, the resulting schedule object must adhere perfectly to your defined Python models.

The `cancel_run` function sends the termination signal. This action is exposed through the MCP Server, providing clear inputs and outputs that your agent validates at runtime.

The `batch_trigger_tasks` tool handles this by grouping calls into one request. The resulting batch results are then validated against your schema, preventing data corruption.

It manages job metadata: run IDs, environment variable names and values, and schedule definitions. All interactions with these types are strictly validated.

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