How to Use the Lyko MCP in LangChain

Q: Does the server support multi-leg trip planning?

The plantrip tool returns complete itineraries including all transfers and modes. Your agent can parse these legs to provide step-by-step instructions for the entire journey.

Build complex transit routing chains in LangChain with the Lyko MCP Server. Connect real-time European transit data to your agents.

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Connect Lyko MCP to LangChain

Create your Vinkius account to connect Lyko 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 Lyko with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain real-time transit data in LangChain

Feed live departures into your agentic logic. Use `get_departures` to feed arrival times into a downstream node, building a chain that automatically adjusts to delays. Your agents handle the heavy lifting. By chaining `plan_trip` with `get_network_status`, your pipeline skips routes affected by active strikes or closures before proposing a path.

Automate complex mobility bookings

Execute end-to-end reservations directly through your workflow. The `book_trip` tool returns the necessary QR codes and payment confirmation for your agent to pass to the next step. Everything stays within the chain. You maintain full observability over every hop in the process, ensuring that ticket data flows correctly from the Lyko API to your final output.

Aggregate multi-server transit data

Combine local stop data with broader network insights. Use `get_nearby_stops` to find your starting point, then pipe that ID into `get_stop_info` to pull accessibility metadata. This setup works across multiple servers. Your agent pulls coordinates from one source and resolves them into transit details using the Lyko MCP Server, keeping your logic modular and clean.

Setup guide

Set up Lyko 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 Lyko 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({
    "lyko-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 Lyko 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 Lyko. 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 Lyko MCP in LangChain

Install the necessary adapters and initialize the client using the provided HTTP endpoint. Once connected, use the client to fetch tool definitions and register them directly with your agent's tool set.

Yes, by calling the status and departure tools, your agent receives the latest ETA and delay data. This allows your chain to re-route or notify users if a scheduled service is running behind.

Absolutely. You can pass the output of any Lyko tool into a LangGraph state object. This ensures your agent remembers the specific trip details or booking IDs generated during the conversation.

The `plan_trip` tool returns complete itineraries including all transfers and modes. Your agent can parse these legs to provide step-by-step instructions for the entire journey.

Your transit requests and booking parameters are transmitted directly to the operator APIs. No PII is stored long-term by the server; it only processes the specific trip and station data needed for your query.

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