TransportAPI MCP Server for LangChain 12 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect TransportAPI through 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({
"transportapi": {
"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 TransportAPI, 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 TransportAPI MCP Server
Connect your TransportAPI UK public transport data platform to any AI agent and take full control of real-time bus and rail tracking, multimodal journey planning, and service disruption monitoring across Great Britain through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with TransportAPI through native MCP adapters. Connect 12 tools via 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
- Real-Time Bus Tracking — Check upcoming departures and arrivals at any UK bus stop with ETAs and delay indicators
- Rail Services — Monitor train arrivals, departures, and services at any UK rail station
- Journey Planning — Plan door-to-door multimodal trips combining bus, rail, tram, underground, walking, and cycling
- Stop Discovery — Search UK bus stops by name, address, or landmark with Naptan identifiers
- Route Analysis — Get train route information between any two UK rail stations with calling points
- Service Updates — Check real-time disruption alerts and operational notices across UK transport networks
- Bus Timetables — Access complete timetables for any UK bus line with weekday/weekend patterns
- Station Information — Get detailed UK rail station data including facilities, accessibility, and managing TOCs
- Stop Details — Retrieve comprehensive bus stop information with served lines and accessibility features
The TransportAPI MCP Server exposes 12 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 TransportAPI to LangChain via MCP
Follow these steps to integrate the TransportAPI 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 12 tools from TransportAPI via MCP
Why Use LangChain with the TransportAPI MCP Server
LangChain provides unique advantages when paired with TransportAPI through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine TransportAPI 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 TransportAPI queries for multi-turn workflows
TransportAPI + LangChain Use Cases
Practical scenarios where LangChain combined with the TransportAPI MCP Server delivers measurable value.
RAG with live data: combine TransportAPI tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query TransportAPI, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain TransportAPI tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every TransportAPI tool call, measure latency, and optimize your agent's performance
TransportAPI MCP Tools for LangChain (12)
These 12 tools become available when you connect TransportAPI to LangChain via MCP:
get_bus_arrivals
Returns list of arriving services with line names and numbers, origins, scheduled and real-time arrival times (ETA), expected wait times, direction, operator details, and any delay indicators. Essential for passenger pickup coordination, arrival monitoring, and real-time arrival boards. AI agents use this when users ask "when is the next bus arriving at this stop", "show incoming buses at stop X", or need to track arriving bus services for passenger coordination. Get real-time bus arrivals at a specific UK stop
get_bus_departures
Returns list of departing services with line names and numbers, destinations, scheduled and real-time departure times (ETD), expected wait times, direction, operator details, and any service disruption notices. Covers all bus services across Great Britain including London Buses, Transport for Greater Manchester, West Midlands, and regional operators. Essential for passenger information displays, departure boards, journey planning, and real-time transit monitoring. AI agents should reference this when users ask "when is the next bus from this stop", "show departures from stop ID X", or need to monitor upcoming bus services at a known UK bus stop. Get real-time bus departures from a specific stop in the UK
get_journey_plan
Supports multimodal trips combining bus, rail, tram, underground (tube), walking, and cycling. Returns complete itinerary with departure and arrival times, total duration, number of changes, legs with mode details (line name, operator, vehicle type), intermediate stops/stations, walking distances, and real-time disruption information. Essential for travel planning, multimodal journey optimization, passenger information systems, and UK-wide mobility applications. AI agents should use this when users ask "how do I get from London Victoria to Heathrow Airport", "plan a journey from Manchester Piccadilly to Old Trafford", or need door-to-door trip planning across UK public transport. Plan a multimodal journey between two UK locations
get_rail_arrivals
Returns list of arriving services with train operating companies, origins, scheduled and real-time arrival times (ETA), platforms, expected delays, cancellation status, and service type information. Covers all National Rail services. Essential for passenger pickup coordination, arrival monitoring, station management, and real-time arrival boards. AI agents use this when users ask "what trains are arriving at Kings Cross", "show incoming trains at Manchester Piccadilly", or need to track arriving rail services. Get real-time train arrivals at a specific UK rail station
get_rail_departures
Returns list of departing services with train operating companies, destinations, scheduled and real-time departure times (ETD), platforms, expected delays, cancellation status, calling points, and service type (express, local, sleeper). Covers all National Rail services across Great Britain. Essential for departure boards, journey planning, station operations, and passenger information. AI agents should use this when users ask "what trains are leaving Paddington", "show departures from Birmingham New Street", or need comprehensive departure listings for a UK rail station. Get real-time train departures from a specific UK rail station
get_rail_route
Returns available services, journey duration, number of changes, calling points, train operating companies, typical frequency, and first/last service times. Essential for rail journey planning, route comparison, travel itinerary preparation, and understanding rail connectivity. AI agents should reference this when users ask "what is the train route from London to Manchester", "show rail connections between Edinburgh and Glasgow", or need to understand rail service options between two UK stations. Get train route information between two UK rail stations
get_rail_services
Returns services with train operating companies (TOCs), destinations, origins, scheduled times, platforms, service types (express, local, sleeper), and any disruption information. Covers National Rail services across Great Britain. Essential for station information displays, service monitoring, rail journey planning, and operational awareness. AI agents should reference this when users ask "what services call at Euston", "show all trains at Edinburgh Waverley", or need comprehensive service listings for a UK rail station. Get all train services calling at a specific UK rail station
get_station_info
Returns station name, location (address, latitude, longitude), facilities (ticket office, ticket machines, waiting room, car park, cycle storage, WiFi, step-free access), staffing hours, managing train operating company, annual entry/exit statistics, and accessibility information. Essential for station planning, accessibility assessment, facility verification, and passenger information. AI agents should use this when users ask "tell me about Clapham Junction station", "does Euston have step-free access", or need detailed station metadata for UK rail journey planning. Get detailed information about a specific UK rail station
get_stop_info
Returns stop name, location (latitude, longitude, address, locality, landmark), common services, served lines, stop type (bus stop, bus station, coach station), accessibility features (wheelchair access, sheltered, seating), and operator information. Essential for stop identification, accessibility planning, transit network analysis, and passenger information. AI agents should use this when users ask "tell me about this bus stop", "what lines serve stop X", or need detailed stop metadata to contextualize transit queries. Get detailed information about a specific UK bus stop
get_timetable
Returns all scheduled services with departure times from origin through to terminus, stops served in sequence, journey duration variations by time of day, weekday/weekend/holiday service patterns, operator information, and any planned service changes. Essential for comprehensive schedule analysis, journey planning at specific times, service pattern research, and understanding bus frequency throughout the day. AI agents use this when users ask "show me the full timetable for bus route 73", "what times does the X59 run on Sundays", or need complete schedule data for a UK bus service. Get full timetable for a specific UK bus line
get_updates
Returns active alerts with affected lines, services, or operators, disruption descriptions, severity levels, expected duration, alternative route recommendations, and timestamps. Covers bus, rail, tram, and underground services across Great Britain. Essential for disruption awareness, passenger communication, journey reliability monitoring, and travel planning during service changes. AI agents should reference this when users ask "are there any disruptions on the Northern Line", "is there engineering work on Great Western Railway", or need to check service reliability before planning UK journeys. Get real-time service updates and disruption alerts for UK transport
search_stops
Returns matching stops with Naptan stop IDs, names, locations (latitude, longitude), served lines, localities, and stop types. Essential for stop discovery, journey planning interfaces, transit stop identification, and building location-based transit features. AI agents should use this when users ask "find the bus stop near Oxford Street", "search for stops called Piccadilly", or need to identify Naptan stop IDs for use in departure/arrival queries. Search for UK bus stops by name, location, or landmark
Example Prompts for TransportAPI in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with TransportAPI immediately.
"Show me all bus departures from Oxford Circus in the next 30 minutes."
"What trains are departing from London Paddington to Bristol in the next 2 hours?"
"Plan a journey from Manchester Airport to the city centre using public transport."
Troubleshooting TransportAPI MCP Server with LangChain
Common issues when connecting TransportAPI to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersTransportAPI + LangChain FAQ
Common questions about integrating TransportAPI 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 TransportAPI 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 TransportAPI to LangChain
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.