How to Use the TransportAPI MCP in LangChain

Q: How does LangChain use TransportAPI to plan routes?

LangChain's ReAct agents can call getjourneyplan first. They figure out the optimal route, and then they might chain a second tool, like getupdates, to check for any current service disruptions on that specific path.

Q: Can I use TransportAPI with LangChain to check service reliability?

Absolutely. The getupdates tool pulls active alerts across bus, rail, and tram services. An agent can call this first using the MCP Server before attempting a journey plan, ensuring the user knows about any known disruptions.

Q: Does TransportAPI support querying multiple stops with LangChain?

Yes. You can use searchstops to find Naptan IDs for several locations, and then feed those IDs into tools like getbusdepartures or getrailarrivals in sequence.

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Connect TransportAPI MCP to LangChain

Create your Vinkius account to connect TransportAPI to LangChain 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 TransportAPI with LangChain

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Perplexity

Model multimodal trip planning

The `get_journey_plan` tool handles trips combining bus, rail, tram, underground, walking, and cycling. You feed it two locations, and it spits out a complete itinerary—times, total duration, changes, even the walking distance between legs. It's perfect for agents that need to solve complex 'how do I get there?' questions across UK public transport networks.

Track real-time bus and train movement

Need to know if a service is running? Use `get_bus_arrivals` or `get_rail_departures`. Both give you line names, numbers, destinations, scheduled times, and the crucial real-time ETA/ETD. You can build an agent that monitors specific stops for incoming vehicles. If your chain needs to know if a train is delayed or if the next bus leaves in five minutes, these tools provide the hard data you need.

Manage station and stop metadata

Don't just rely on time. Use `get_station_info` to pull detailed details about a UK rail station—like whether it has step-free access, or if there’s a cycle storage. Similarly, `get_stop_info` gives you the core data for any bus stop. These tools let your agent validate location context before planning anything, which is essential when combining multiple MCP Server calls in one chain.

Setup guide

Set up TransportAPI MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes TransportAPI tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "transportapi-mcp": {
        "transport": "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,
    )
    result = await agent.ainvoke({
        "messages": "List recent TransportAPI transactions"
    })
    print(result["messages"][-1].content)

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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 TransportAPI. 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 TransportAPI MCP in LangChain

LangChain's ReAct agents can call `get_journey_plan` first. They figure out the optimal route, and then they might chain a second tool, like `get_updates`, to check for any current service disruptions on that specific path.

It gives structured JSON containing multimodal itinerary details. This includes departure/arrival times, total duration in minutes, and a breakdown of the mode for every leg—whether it's tube, bus, or walking.

Absolutely. The `get_updates` tool pulls active alerts across bus, rail, and tram services. An agent can call this first using the MCP Server before attempting a journey plan, ensuring the user knows about any known disruptions.

Yes. You can use `search_stops` to find Naptan IDs for several locations, and then feed those IDs into tools like `get_bus_departures` or `get_rail_arrivals` in sequence.

This server handles public transit metadata. It touches non-personal service information, specifically station names and stop IDs (Naptan IDs).

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