How to Use the Trigger.dev MCP in LlamaIndex

Q: Can I use Trigger.dev for scheduled tasks in LlamaIndex?

Yes, you call listschedules. This populates your knowledge base with information about recurring workflows, letting users ask questions like 'When is the nightly report run?'

Q: If I build a RAG application using Trigger.dev and LlamaIndex, what data do I index?

You index operational records—things like project names (listprojects), environment details, and task execution logs. This makes the system answer questions about live API data.

Q: What is the process for using Trigger.dev with LlamaIndex?

You pass the available tools (like cancelrun or listprojects) to your function agent. The agent then uses the tool output to augment its answer before querying the index.

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Connect Trigger.dev MCP to LlamaIndex

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Setup Trigger.dev with LlamaIndex

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Querying Task Run History via MCP Server

The `get_run` tool lets your RAG application pull specific task details. Instead of just getting a status code, you index the run's inputs and outputs into your vector store. Need to understand what ran? Use `list_runs`. The output from this call becomes part of a searchable knowledge base for your users.

Managing Projects and Environments

When building RAG applications, context is everything. Querying `list_projects` gives your index the structure of all managed workflows. Similarly, `list_environments` adds detail about deployment stages. This allows you to ground answers in actual API data from Trigger.dev, not just documents.

Triggering and Replaying Tasks

Your knowledge base can answer questions like, 'What happens if I run X again?' Use `trigger_task` to execute a new job, then index the resulting data. The tool also lets you `replay_run` a specific task for documentation purposes. This makes your system self-correcting and highly accurate.

Setup guide

Set up Trigger.dev MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Trigger.dev MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

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

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Trigger.dev tools.",
)
response = await agent.run("List recent Trigger.dev data")

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Common questions about Trigger.dev MCP in LlamaIndex

LlamaIndex treats the MCP tool output as raw data to be indexed into a vector store. You can then query past configurations and task results semantically.

Yes, you call `list_schedules`. This populates your knowledge base with information about recurring workflows, letting users ask questions like 'When is the nightly report run?'

You index operational records—things like project names (`list_projects`), environment details, and task execution logs. This makes the system answer questions about live API data.

You pass the available tools (like `cancel_run` or `list_projects`) to your function agent. The agent then uses the tool output to augment its answer before querying the index.

The server exposes details on project structure, specific deployment environments, and task run status. All of this operational metadata can be added to your searchable knowledge base.

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