How to Use the HERE Mobility MCP in LangChain

Q: Can my LangChain agent handle trips with both walking and bus routes using HERE Mobility?

Yes. The searchmultimodaltrips tool is built for this. Your agent calls it with an origin and destination, and HERE Mobility returns options that mix transit and walking, which the agent can then parse and present.

Q: What's the best way to find a specific station like 'Grand Central' with HERE Mobility and LangChain?

Use the getstationsbyname tool. Your agent passes the string 'Grand Central' and gets back a list of matching stations with their IDs. This is much more reliable than guessing coordinates for common landmarks.

Build multi-step transit planning agents with LangChain. Chain HERE Mobility API calls to create complex routes from scratch.

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Connect HERE Mobility MCP to LangChain

Create your Vinkius account to connect HERE Mobility 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 HERE Mobility with LangChain

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ChatGPT

Claude

Perplexity

Chain Station Lookups and Trip Discovery

Your LangChain agent can build a trip plan step-by-step. First, it finds the closest station to a user's location with `get_nearby_stations`. Then, it feeds that station ID directly into `get_schedule` to check departure times. This isn't just one API call; it's a reasoning chain where the agent decides what's next. If the first station is wrong, it can use `get_stations_by_name` as a fallback. You see every step, input, and output in LangSmith, so debugging is straightforward.

Construct Multimodal Routes with LangChain

Go beyond simple public transit. An agent can call `search_multimodal_trips` to find a route that combines a train ride with a final-mile scooter trip. The tool returns a breakdown of each leg of the journey. Your agent can then parse that output and use `get_route_details` on just the transit portion to get more specific information, like transfer points. It's about building agents that handle real-world complexity, not just single-shot queries.

Dynamic Itinerary Planning

A user's plan changes, and your agent can adapt. After initially finding a route with `discover_trips`, it can continuously poll `get_schedule` to watch for delays. If a delay is detected, the agent can automatically re-run `discover_trips` with an updated departure time to find an alternative. This creates a responsive system that handles real-time transit issues, all orchestrated by your LangChain agent and this MCP Server.

Setup guide

Set up HERE Mobility 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 HERE Mobility 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({
    "here-mobility-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 HERE Mobility 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 HERE Mobility. 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 HERE Mobility MCP in LangChain

You pass the tool list from this MCP server to a LangChain agent. The agent then intelligently sequences calls, for example, using the output station ID from `get_nearby_stations` as the input for `get_schedule` to create a dynamic, multi-step plan.

Yes. The `search_multimodal_trips` tool is built for this. Your agent calls it with an origin and destination, and HERE Mobility returns options that mix transit and walking, which the agent can then parse and present.

Use the `get_stations_by_name` tool. Your agent passes the string 'Grand Central' and gets back a list of matching stations with their IDs. This is much more reliable than guessing coordinates for common landmarks.

Every tool call your LangChain agent makes is an observable event. When you use a tracer like LangSmith, you get a complete log of inputs and outputs for each step, making it easy to see why your agent chose one route over another.

The server primarily processes location data—like origin/destination coordinates or station names—that your agent sends. Vinkius runs each server in an ephemeral, zero-trust sandbox. Your connection is authenticated, and the server instance is wiped after use, so no trip data is stored.

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