How to Use the Deputy MCP in OpenAI Agents SDK

Q: Can my OpenAI Agents SDK agent update employee profiles?

The server provides getemployeeprofile for reading data. You cannot modify records through this MCP Server, keeping your Deputy data state consistent.

Connect Deputy to your OpenAI Agents SDK project to manage shifts and time-off directly from your production agent code.

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Connect Deputy MCP to OpenAI Agents SDK

Create your Vinkius account to connect Deputy 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 Deputy with OpenAI Agents SDK

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Real-time shift visibility for OpenAI Agents SDK

Your agent pulls active roster data to see who is working right now. It uses `list_active_rosters` to grab current schedules without a browser. This keeps your agent informed about workforce status. It connects your scheduling data to your logic flow instantly.

Automated leave request processing with OpenAI

Stop digging through dashboards to find pending time-off requests. Your agent checks `list_pending_leave_approvals` to identify what needs attention. It flags these items for your review inside the agent trace. You catch bottlenecks before they become scheduling headaches.

Identity verification for Deputy within your agent

The agent confirms its own access using `get_authenticated_user` before running sensitive tasks. This ensures every API call originates from a verified source. It verifies the current user context during your agent runtime. You maintain clean logs for every action the system performs.

Setup guide

Set up Deputy 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 Deputy tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Deputy 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="Deputy Agent",
            instructions="You have access to Deputy 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 Deputy MCP in OpenAI Agents SDK

You add the server by initializing an MCPServerStreamableHttp instance with your endpoint. Pass that into your agent constructor to let the framework auto-discover the tools.

The server provides `get_employee_profile` for reading data. You cannot modify records through this MCP Server, keeping your Deputy data state consistent.

The SDK performs strict validation on all tool outputs. If the Deputy API sends malformed JSON, the agent stops execution to prevent logic errors.

We only expose the specific employee metadata and shift records you query. Your credentials stay on the Vinkius managed host, so the agent never touches raw API keys.

The server only touches public workforce metadata like names and roster slots. It does not store PII, and every connection requires an endpoint token to function.

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