How to Use the Navitia MCP in LangChain

Q: How do I connect the Navitia MCP Server to LangChain?

Install langchain-mcp-adapters and use MultiServerMCPClient. Pass the HTTP transport URL for the Navitia server, call gettools(), and inject them into your agent builder.

Q: Can LangChain agents handle real-time delays with Navitia?

Yes. Your agent can call getdepartures or getarrivals with the datafreshness parameter set to realtime. It will read the actual operator feeds instead of the theoretical timetable.

Q: How does Navitia handle station searches in a LangChain pipeline?

You build a chain that starts with searchplaces. The agent searches for a text string, extracts the correct transit stop ID from the JSON response, and passes it downstream to the schedule tools.

Q: What transit modes can my LangChain agent access?

The getlines and getnetworks tools expose metros, buses, trams, RER, and high-speed rail. Your agent filters these results to restrict routing options based on user preferences.

Build multi-step transit reasoning pipelines with LangChain. Chain real-time European train schedules directly into your agents.

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Connect Navitia MCP to LangChain

Create your Vinkius account to connect Navitia 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 Navitia with LangChain

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Perplexity

Plan multimodal routes with LangChain agents

The `plan_journey` tool lets your LangChain agent map out complex trips across Europe using public transit, walking, or ridesharing. Instead of hardcoding routes, your ReAct agent decides how to handle transfers based on user profiles like wheelchair accessibility or walking speed. You pass the user's origin and destination through `search_places` to get the exact stop IDs, then feed those directly into the journey planner. LangSmith tracks the exact token usage and latency for every API call while the agent builds the itinerary.

Chain disruption alerts into your MCP Server

The `get_disruptions` tool pulls active service alerts, strike information, and maintenance delays for specific transit networks. Your agent grabs this data before executing a routing chain to ensure it doesn't suggest a canceled train. If a major incident blocks a route, the agent automatically falls back to `get_nearby_stops` to find alternative stations. This creates a resilient routing pipeline where the system actively avoids bad transit data.

Calculate transit catchments dynamically

The `get_isochrone` tool generates GeoJSON polygons showing exactly how far a user can travel from a specific point within a set time limit. You can pipe this output into a LangChain vector store to filter real estate listings based on commute times. When a user asks for apartments within 30 minutes of Paris, the agent runs the isochrone calculation first. It uses the resulting geographic boundaries to query your database, merging transit constraints with your custom application logic.

Setup guide

Set up Navitia 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 Navitia 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({
    "navitia-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 Navitia transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Navitia MCP in LangChain

Install `langchain-mcp-adapters` and use `MultiServerMCPClient`. Pass the HTTP transport URL for the Navitia server, call `get_tools()`, and inject them into your agent builder.

Yes. Your agent can call `get_departures` or `get_arrivals` with the `data_freshness` parameter set to realtime. It will read the actual operator feeds instead of the theoretical timetable.

You build a chain that starts with `search_places`. The agent searches for a text string, extracts the correct transit stop ID from the JSON response, and passes it downstream to the schedule tools.

The `get_lines` and `get_networks` tools expose metros, buses, trams, RER, and high-speed rail. Your agent filters these results to restrict routing options based on user preferences.

The MCP Server only processes geographic coordinates and transit stop IDs to execute the immediate request. Vinkius runs the integration inside an ephemeral V8 Isolate Sandbox, ensuring raw location data never leaks into persistent logs.

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