Transport for London MCP Server for LangChain 11 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Transport for London through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with MultiServerMCPClient({
"transport-for-london": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Transport for London, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
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.
LangChain's ecosystem of 500+ components combines seamlessly with Transport for London through native MCP adapters. Connect 11 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
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 LangChain 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 LangChain via MCP
Follow these steps to integrate the Transport for London MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 11 tools from Transport for London via MCP
Why Use LangChain with the Transport for London MCP Server
LangChain provides unique advantages when paired with Transport for London through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents — combine Transport for London MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Transport for London queries for multi-turn workflows
Transport for London + LangChain Use Cases
Practical scenarios where LangChain combined with the Transport for London MCP Server delivers measurable value.
RAG with live data: combine Transport for London tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Transport for London, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Transport for London tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Transport for London tool call, measure latency, and optimize your agent's performance
Transport for London MCP Tools for LangChain (11)
These 11 tools become available when you connect Transport for London to LangChain 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 LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting Transport for London to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersTransport for London + LangChain FAQ
Common questions about integrating Transport for London MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
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 LangChain
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.