Transport for London MCP Server for Pydantic AI 11 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Transport for London through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to Transport for London "
"(11 tools)."
),
)
result = await agent.run(
"What tools are available in Transport for London?"
)
print(result.data)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Transport for London MCP Server
Connect to Transport for London (TfL) and access real-time London transit data through natural conversation — no API key needed.
Pydantic AI validates every Transport for London tool response against typed schemas, catching data inconsistencies at build time. Connect 11 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.
What you can do
- Tube Status — Check real-time status of all Underground lines (Good Service, Minor/Severe Delays, Suspended)
- Line Details — Get detailed info about any tube, overground, DLR, Elizabeth line or tram route
- Bus Arrivals — Get live bus arrival predictions for any stop
- Journey Planning — Plan journeys between any two London locations with step-by-step directions
- Road Status — Check major road status and disruptions across London
- Bike Points — Find Santander Cycle docking stations with bike and dock availability
- Stop Search — Search for bus stops, tube stations and river piers by name
The Transport for London MCP Server exposes 11 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Transport for London to Pydantic AI via MCP
Follow these steps to integrate the Transport for London MCP Server with Pydantic AI.
Install Pydantic AI
Run pip install pydantic-ai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 11 tools from Transport for London with type-safe schemas
Why Use Pydantic AI with the Transport for London MCP Server
Pydantic AI provides unique advantages when paired with Transport for London through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your Transport for London integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Transport for London connection logic from agent behavior for testable, maintainable code
Transport for London + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Transport for London MCP Server delivers measurable value.
Type-safe data pipelines: query Transport for London with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Transport for London tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Transport for London and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Transport for London responses and write comprehensive agent tests
Transport for London MCP Tools for Pydantic AI (11)
These 11 tools become available when you connect Transport for London to Pydantic AI via MCP:
get_arrivals
Returns predicted arrival times, destination, line number, vehicle ID and expected time to station. Use the stop point ID (e.g. "490009056W") from search_stop. Get live arrival predictions for a bus stop
get_bike_point_detail
Get detailed info for a specific bike docking station
get_bike_points
Returns bike availability, dock availability, station locations and status. Useful for finding nearby bikes for cycling journeys. Search for Santander Cycle (Boris Bike) docking stations
get_journey
Returns multiple route options with estimated duration, walking distance, fare cost, number of changes and step-by-step directions. Input locations can be station names, addresses or postcodes. Plan a journey between two points in London
get_line_detail
Supports tube, overground, DLR, Elizabeth line, tram and river bus lines. Get detailed information about a specific TfL line
get_line_routes
Returns the ordered list of stations the line serves. Useful for understanding the full journey path of a tube line. Get the route sequence for a TfL line
get_line_status
Shows whether each line has Good Service, Minor Delays, Severe Delays, or is Suspended/Part Suspended. If no line IDs specified, returns all tube lines. Use line_ids to check specific lines (comma-separated, e.g. "central,victoria,northern"). Get real-time status for TfL tube lines
get_road_disruptions
Returns disruption details with severity, location, cause and estimated clearance times. Get current road disruptions in London
get_road_status
Shows whether roads have Good, Minor or Severe congestion. Get status of London major roads
get_stop_details
Useful for identifying the correct stop ID for arrival queries. Get details for a specific bus stop or station
search_stop
Returns matching stops with their IDs, locations, modes and routes. Use the returned IDs with get_arrivals or get_stop_details. Search for bus stops and stations by name
Example Prompts for Transport for London in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Transport for London immediately.
"What's the status of the Central line?"
"Plan a journey from King's Cross to Heathrow."
"When is the next bus at Oxford Circus?"
Troubleshooting Transport for London MCP Server with Pydantic AI
Common issues when connecting Transport for London to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiTransport for London + Pydantic AI FAQ
Common questions about integrating Transport for London MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.Does Pydantic AI validate MCP tool responses?
Can I switch LLM providers without changing MCP code?
Connect Transport for London with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
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GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Transport for London to Pydantic AI
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.