Transport for London MCP Server for OpenAI Agents SDK 11 tools — connect in under 2 minutes
The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Transport for London through the Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails — no manual schema definitions required.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with MCPServerStreamableHttp(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
) as mcp_server:
agent = Agent(
name="Transport for London Assistant",
instructions=(
"You help users interact with Transport for London. "
"You have access to 11 tools."
),
mcp_servers=[mcp_server],
)
result = await Runner.run(
agent, "List all available tools from Transport for London"
)
print(result.final_output)
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.
The OpenAI Agents SDK auto-discovers all 11 tools from Transport for London through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns — chain multiple agents where one queries Transport for London, another analyzes results, and a third generates reports, all orchestrated through the Vinkius.
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 OpenAI Agents SDK 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 OpenAI Agents SDK via MCP
Follow these steps to integrate the Transport for London MCP Server with OpenAI Agents SDK.
Install the SDK
Run pip install openai-agents in your Python environment
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com
Run the script
Save the code above and run it: python agent.py
Explore tools
The agent will automatically discover 11 tools from Transport for London
Why Use OpenAI Agents SDK with the Transport for London MCP Server
OpenAI Agents SDK provides unique advantages when paired with Transport for London through the Model Context Protocol.
Native MCP integration via `MCPServerSse` — pass the URL and the SDK auto-discovers all tools with full type safety
Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure
Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate
First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output
Transport for London + OpenAI Agents SDK Use Cases
Practical scenarios where OpenAI Agents SDK combined with the Transport for London MCP Server delivers measurable value.
Automated workflows: build agents that query Transport for London, process the data, and trigger follow-up actions autonomously
Multi-agent orchestration: create specialist agents — one queries Transport for London, another analyzes results, a third generates reports
Data enrichment pipelines: stream data through Transport for London tools and transform it with OpenAI models in a single async loop
Customer support bots: agents query Transport for London to resolve tickets, look up records, and update statuses without human intervention
Transport for London MCP Tools for OpenAI Agents SDK (11)
These 11 tools become available when you connect Transport for London to OpenAI Agents SDK 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 OpenAI Agents SDK
Ready-to-use prompts you can give your OpenAI Agents SDK 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 OpenAI Agents SDK
Common issues when connecting Transport for London to OpenAI Agents SDK through the Vinkius, and how to resolve them.
MCPServerStreamableHttp not found
pip install --upgrade openai-agentsAgent not calling tools
Transport for London + OpenAI Agents SDK FAQ
Common questions about integrating Transport for London MCP Server with OpenAI Agents SDK.
How does the OpenAI Agents SDK connect to MCP?
MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.Can I use multiple MCP servers in one agent?
MCPServerSse instances to the agent constructor. The agent can use tools from all connected servers within a single run.Does the SDK support streaming responses?
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.
AI-first code editor with integrated LLM-powered coding assistance.
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 OpenAI Agents SDK
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.