How to Use the Cedar AI MCP in OpenAI Agents SDK

Track rail logistics in your OpenAI Agents SDK apps by connecting Cedar AI directly to your agent's decision loop.

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Connect Cedar AI MCP to OpenAI Agents SDK

Create your Vinkius account to connect Cedar AI 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 Cedar AI with OpenAI Agents SDK

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Automate train status tracking

Your agent updates yard logs instantly using `arrive_train` and `depart_train`. It captures movement data without manual entry. Python agents built on this stack execute these calls within your defined guardrails. You get precise records of every movement inside your pipeline.

Manage railcar inventory via OpenAI Agents SDK

Run `list_inventory` to pull a snapshot of your current railcars. Your agent parses this data to maintain an accurate count. Each railcar status change happens through `update_railcar_status`. This keeps your database in sync with physical yard reality.

Process complex rail documentation

The `get_waybill_details` tool extracts specific data points for your agent to review. It reads the waybill so you don't have to. Work orders move faster when your agent triggers `get_work_order_details`. You define the logic for how the agent handles these documents.

Setup guide

Set up Cedar AI 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 Cedar AI tools at runtime.
3

Create your Agent

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

Run the agent

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

You initialize the connection using the MCPServerStreamableHttp class. Pass your Vinkius endpoint into the constructor and add the server to your Agent instance.

Yes. Your agent uses built-in tracing to catch failed tool calls. You see exactly where a request to Cedar AI might have hit a snag.

It is designed for high-uptime environments. Your agent performs reliable lookups on rail data using standard Python async patterns.

No. Once you configure the transport, the tools appear in your agent's registry. You get immediate access to all twelve rail operations.

All traffic stays inside a V8 sandbox. We never store your waybill or inventory records after the transaction finishes.

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