How to Use the TransportAPI MCP in Pydantic AI

Q: How do I use TransportAPI Alternative and Pydantic AI for journey planning?

You call planpublictransportjourney and pass the locations. Because of Pydantic's validation, you can trust that the step-by-step route returned by the MCP Server is perfectly structured and reliable.

Q: Can TransportAPI Alternative handle live departures with Pydantic AI?

Yep. You use getlivebusdepartures or getlivetraindepartures. The agent receives clean, validated data on time, destination, and operator, preventing runtime errors.

Q: Does TransportAPI Alternative help find nearby stops with Pydantic AI?

Yes. You can use getnearbybusstops or getnearbytransportplaces. The tool output is validated, so you know exactly what structure to expect when reading the location data.

Q: Is it possible to check train schedules with TransportAPI Alternative and Pydantic AI?

You can call gettrainstationtimetable or getbusroutetimetable. The MCP Server provides the schedule, and Pydantic ensures that the data structure is always correct for consumption.

Type-Safe UK Transit: Process TransportAPI Alternative with Pydantic AI

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Setup TransportAPI with Pydantic AI

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Live Departures

To check real-time departures, call `get_live_bus_departures` or `get_live_train_departures`. These tools return structured data that Pydantic validates against defined models. You won't get unexpected fields. The MCP Server also supports getting nearby locations via `get_nearby_transport_places`, ensuring the coordinates and results fit your expected Python schema.

Journey Planning

`plan_public_transport_journey` returns a step-by-step route, validating every time point and change. This guarantees that complex travel data is correct before it hits your application logic. The framework also lets you use `get_transport_by_postcode`, which ensures the output structure for postcodes is always predictable.

Fares and Schedules

When comparing costs, run `get_train_fares`. Since every response validates against a Pydantic model, you're sure to get clean data about the fares. You can also pull full schedules using `get_bus_route_timetable` or `get_train_station_timetable`, knowing that the returned timetable format is rock solid.

Setup guide

Set up TransportAPI MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "transportapi-alternative-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to TransportAPI tools.",
)

result = await agent.run("List recent TransportAPI transactions")
print(result.output)

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Common questions about TransportAPI MCP in Pydantic AI

You call `plan_public_transport_journey` and pass the locations. Because of Pydantic's validation, you can trust that the step-by-step route returned by the MCP Server is perfectly structured and reliable.

Yep. You use `get_live_bus_departures` or `get_live_train_departures`. The agent receives clean, validated data on time, destination, and operator, preventing runtime errors.

The server deals with location data—GPS coordinates and UK postcodes. By validating these fields, your agent gets confidence that the inputs are clean before processing.

Yes. You can use `get_nearby_bus_stops` or `get_nearby_transport_places`. The tool output is validated, so you know exactly what structure to expect when reading the location data.

You can call `get_train_station_timetable` or `get_bus_route_timetable`. The MCP Server provides the schedule, and Pydantic ensures that the data structure is always correct for consumption.

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