How to Use the Transport for London MCP in Pydantic AI

Q: Can I use Pydantic AI for Transport for London journey planning?

Yes, you can call getjourney and then validate the resulting list of route options. This ensures that every element—like the fare cost or number of changes—conforms to your expected data model.

Q: Does the Pydantic AI framework help with multiple Transport for London lines?

It does. When you query getlinestatus for several IDs, validation ensures that every single status update returned is structured identically and correctly.

Q: What kind of real-time data can Pydantic AI process from Transport for London?

You get road congestion levels via getroadstatus or specific disruption details. Because the output is strictly typed, you know exactly what fields to expect.

Ensure flawless data validation for Transport for London with Pydantic AI.

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Connect Transport for London MCP to Pydantic AI

Create your Vinkius account to connect Transport for London to Pydantic AI 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 Transport for London with Pydantic AI

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Perplexity

Predict bus arrivals with strict typing

The agent calls `get_arrivals`, and the results are validated against a Pydantic model. You expect specific fields like predicted arrival times, destination, and line number—if the API sends anything else, the agent fails loudly. This guarantees that your client code only receives clean, predictable data structures for bus stops.

Map precise TFL routes and sequence

Need to know exactly which stations a line serves? `get_line_routes` provides the ordered list of stations. This is crucial because Pydantic validation ensures that the output array is correctly structured for downstream processing. It also lets you get detailed information about any specific TFL line type, like tram or DLR.

Report disruptions and road status

When a problem hits, your agent uses `get_road_disruptions` to pull disruption details. The strict validation confirms that the severity, location, cause, and estimated clearance times are all present and correctly typed. Similarly, checking line health with `get_line_status` guarantees you receive accurate status levels (Good Service, Minor Delays, etc.).

Setup guide

Set up Transport for London 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": {
        "transport-for-london-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Transport for London transactions")
print(result.output)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Transport for London. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Transport for London MCP in Pydantic AI

The MCP Server outputs are validated against defined Python schemas. This means that even if the underlying TFL API changes, your agent fails predictably instead of silently corrupting your data.

Yes, you can call `get_journey` and then validate the resulting list of route options. This ensures that every element—like the fare cost or number of changes—conforms to your expected data model.

The server handles public transit metadata, specifically including line status updates and predicted arrival times at bus stops. It never touches personal passenger data.

It does. When you query `get_line_status` for several IDs, validation ensures that every single status update returned is structured identically and correctly.

You get road congestion levels via `get_road_status` or specific disruption details. Because the output is strictly typed, you know exactly what fields to expect.

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