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 LlamaIndex 8 tools — connect in under 2 minutes

Name: Trigger.dev
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 8 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Trigger.dev as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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 llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Trigger.dev. "
            "You have 8 tools available."
        ),
    )

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

asyncio.run(main())

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* 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.

LlamaIndex agents combine Trigger.dev tool responses with indexed documents for comprehensive, grounded answers. Connect 8 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

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 LlamaIndex 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 LlamaIndex via MCP

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

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

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

Why Use LlamaIndex with the Trigger.dev MCP Server

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

Data-first architecture: LlamaIndex agents combine Trigger.dev tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Trigger.dev tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Trigger.dev, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Trigger.dev tools were called, what data was returned, and how it influenced the final answer

Trigger.dev + LlamaIndex Use Cases

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

Hybrid search: combine Trigger.dev real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Trigger.dev to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Trigger.dev for fresh data

Analytical workflows: chain Trigger.dev queries with LlamaIndex's data connectors to build multi-source analytical reports

Trigger.dev MCP Tools for LlamaIndex (8)

These 8 tools become available when you connect Trigger.dev to LlamaIndex 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 LlamaIndex

Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Trigger.dev + LlamaIndex FAQ

Common questions about integrating Trigger.dev MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Trigger.dev tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.