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The AI Agents and Prompts surface ships as part of the v4.5 release candidate. Install with @trigger.dev/sdk@rc (or pin 4.5.0-rc.0 or later) to use these features — they aren’t yet on the latest stable, and APIs may still change before the 4.5.0 GA. See supported AI SDK versions and the AI chat changelog for details.
The Trigger.dev MCP server includes tools for having conversations with your chat agents directly from AI coding tools like Claude Code, Cursor, Windsurf, and others. This lets your AI assistant interact with your agents without writing any code.

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Available tools

ToolDescription
list_agentsList all agents in the current worker
start_agent_chatStart a conversation with an agent
send_agent_messageSend a message and get the response
close_agent_chatClose a conversation
See the MCP Tools Reference for full details on each tool.

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Typical workflow

1

List available agents

Ask your AI assistant to list agents in your project. This calls list_agents which returns all tasks created with chat.agent() or chat.customAgent().
2

Start a chat

Start a conversation with an agent using start_agent_chat. This triggers a run and optionally preloads the agent so it’s ready to respond immediately.If the agent uses withClientData, pass the required fields via clientData.
3

Send messages

Send messages with send_agent_message. The agent processes each message and returns the full response text, including which tools were used. The agent retains full context across messages in the same chat.
4

Close the chat

When done, close the conversation with close_agent_chat. This signals the agent to exit gracefully. If you skip this step, the agent will close on its own when its idle timeout expires.

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Example conversation

Here’s what a typical MCP conversation with an agent looks like: 
You: List my agents

AI: Found 3 agents in worker 20250402.12 (dev):
    - support-agent (src/trigger/support.ts)
    - pr-review (src/trigger/pr-review.ts)
    - research (src/trigger/research.ts)

You: Start a chat with the support agent

AI: Agent chat started and preloaded.
    - Chat ID: abc-123
    - Agent: support-agent
    - Run ID: run_xyz

You: Ask it to look up order #4821

AI: I found order #4821. It was placed on March 15 and shipped via
    FedEx (tracking: 7891234). Current status: In Transit, estimated
    delivery March 19.

    Tools used: lookupOrder, getShippingStatus

You: Close the chat

AI: Chat abc-123 closed.

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Setup

If you haven’t set up the MCP server yet, see the MCP Server introduction for installation and client configuration. Agent chat tools require:

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How it works

Under the hood, the MCP tools use the same protocol as the frontend transport and AgentChat SDK:
  1. start_agent_chat triggers a task run with the preload trigger and stores the session (run ID, chat ID) in memory.
  2. send_agent_message sends the message via the run’s input stream and subscribes to the output SSE stream to collect the agent’s full response.
  3. close_agent_chat sends a close signal via the input stream and removes the session.
Sessions are held in-memory within the MCP server process. If the MCP server restarts, active sessions are lost — but the underlying agent runs continue until their idle timeout.
The get_current_worker tool also labels agents with [agent] in its output, making it easy to identify which tasks are agents even when listing all tasks.

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See also