How to Use the TransportAPI MCP in OpenAI Agents SDK

Q: How does TransportAPI help with complex routing in OpenAI Agents SDK?

The getjourneyplan tool handles multimodal trips combining bus, rail, underground, walking, and cycling. It returns a complete itinerary detailing departure and arrival times for the entire journey.

Q: Can I check departures using TransportAPI with OpenAI Agents SDK?

Yes. The getbusdepartures tool lists upcoming services from a specific stop, giving you line names, destinations, and real-time ETDs. You simply call the appropriate function passing in the required stop ID.

Q: Does TransportAPI handle service alerts for OpenAI Agents SDK?

Absolutely. The getupdates tool gathers active alerts across all transport modes—bus, rail, tram, and tube. This lets your agent warn users about disruptions before they plan their journey.

Q: How do I search for stops using TransportAPI with OpenAI Agents SDK?

You use searchstops to find matching bus stops by name or location. It returns Naptan stop IDs, names, served lines, and localities, which you then feed into the other departure/arrival tools.

Real-time UK public transit data for your OpenAI Agents SDK workflow.

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Connect TransportAPI MCP to OpenAI Agents SDK

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

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Perplexity

Plan complex, multimodal trips.

The `get_journey_plan` tool maps out door-to-door travel across the UK. You get a full itinerary covering bus, rail, tram, tube, and walking legs. It details total duration, how many transfers you need, and real-time disruption notices along the way. This is crucial for building robust passenger information systems that don't just guess at connections. Your agent handles complex routing by passing start and end points to `get_journey_plan`.

Monitor live arrivals for buses and trains.

`get_bus_arrivals` provides a list of incoming bus services, giving you line names, numbers, expected wait times, and real-time ETAs. It also tracks operator details and flags any delays. Similarly, `get_rail_arrivals` handles train tracking at UK stations. You feed these tools the stop ID or station name, and your agent gets back immediate data points for arrival monitoring boards.

Handle service disruptions across modes.

`get_updates` returns active alerts affecting buses, rails, trams, and underground services. It provides disruption descriptions, severity levels, expected duration, and even alternative route recommendations. When planning a journey using the MCP Server, your agent can cross-reference `get_updates` against a planned trip to warn the user about known service reliability issues before they leave home.

Setup guide

Set up TransportAPI 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 TransportAPI tools at runtime.
3

Create your Agent

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

Run the agent

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

The `get_journey_plan` tool handles multimodal trips combining bus, rail, underground, walking, and cycling. It returns a complete itinerary detailing departure and arrival times for the entire journey.

Yes. The `get_bus_departures` tool lists upcoming services from a specific stop, giving you line names, destinations, and real-time ETDs. You simply call the appropriate function passing in the required stop ID.

Absolutely. The `get_updates` tool gathers active alerts across all transport modes—bus, rail, tram, and tube. This lets your agent warn users about disruptions before they plan their journey.

This MCP Server touches location coordinates (latitude/longitude), stop IDs, line names, train operating companies, and scheduled service times. This metadata powers all the travel planning tools.

You use `search_stops` to find matching bus stops by name or location. It returns Naptan stop IDs, names, served lines, and localities, which you then feed into the other departure/arrival tools.

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