How to Use the Tray.io MCP in OpenAI Agents SDK

Q: How do I check my workflow status using Tray.io and OpenAI Agents SDK?

You use the listworkflowexecutions tool to grab recent run data. The agent pulls this execution history, letting you confirm if a specific automation ran correctly or diagnose why it might have failed.

Q: How is data privacy handled when using Tray.io and OpenAI Agents SDK?

The server accesses authentication details via getauthenticateduser. This means the agent can read user identifying information, so you need to manage permissions carefully in your deployment.

Build Production Agents with OpenAI Agents SDK. Orchestrate automations across Tray.io.

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Connect Tray.io MCP to OpenAI Agents SDK

Create your Vinkius account to connect Tray.io to OpenAI Agents SDK 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 Tray.io with OpenAI Agents SDK

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Manage User Context via MCP Server

The `get_authenticated_user` tool fetches the details of whoever is running the agent right now. This means your agents always know who they're talking to, which is key for personalized workflows. You can build logic that responds specifically to user roles or identifiers pulled from these returned credentials.

Track Automation Status with Tray.io

Need to see if a workflow ran successfully? Use `list_workflow_executions` to pull recent history for any specific automation. This lets your agent check the actual run time and data output, not just whether it was triggered. It's perfect for building monitoring agents that need to report on success rates or find out why an execution failed.

Discover Available Integrations

The `list_available_connectors` tool gives your agent a real-time inventory of all service connectors available in Tray.io, like Salesforce or Slack. This means the agent can plan actions involving specific third-party services. It's how you build agents that don't just operate inside Tray.io but coordinate with external enterprise tools.

Setup guide

Set up Tray.io MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Tray.io tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Tray.io tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Tray.io tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Tray.io Agent",
            instructions="You have access to Tray.io tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Tray.io MCP in OpenAI Agents SDK

You use the `list_workflow_executions` tool to grab recent run data. The agent pulls this execution history, letting you confirm if a specific automation ran correctly or diagnose why it might have failed.

Absolutely. By exposing these tools via the MCP Server, your agent automatically discovers every function—from listing all workflows (`list_workflows`) to checking user details—without you having to write boilerplate code.

The server handles authentication data, connector lists, workflow definitions, and execution records. It gives your agent visibility into the core operational state of your automations.

Yes. Since you're building a deployed product, the built-in guardrails validate every action before it runs, keeping your agent safe from unexpected API calls.

The server accesses authentication details via `get_authenticated_user`. This means the agent can read user identifying information, so you need to manage permissions carefully in your deployment.

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