How to Use the TomTom Parking Availability MCP in LangChain

Q: Is persistent context available when using TomTom Parking Availability with LangChain?

You can maintain a session context using client.session(). This lets subsequent tool calls reference previous search results or configurations, improving continuity.

Give LangChain reliable parking data for complex agent decisions.

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Connect TomTom Parking Availability MCP to LangChain

Create your Vinkius account to connect TomTom Parking Availability 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 TomTom Parking Availability with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Search for spots using the MCP Server

Your agent needs to know where to start looking. Use `search_parking_spots` to find available parking in a given area, returning immediate location data. This function lets your AI client perform initial sweeps across neighborhoods. The output feeds directly into the next step of your chain: filtering results by specific criteria like height or type.

Getting full details with LangChain

Once a spot is identified, you need specifics before committing to it. Calling `get_parking_details` retrieves all the availability metadata for a given parking ID. Your agent uses this data—like maximum capacity or specific opening hours—to make a final judgment call within the multi-step pipeline.

Checking the service status

Before running any complex search, you gotta know if the source is reliable. The `check_api_status` tool verifies that the TomTom Parking Availability service is up and running. This simple check prevents your agent from wasting time on calls that are going to fail due to an external outage.

Setup guide

Set up TomTom Parking Availability 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 TomTom Parking Availability 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({
    "tomtom-parking-availability-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 TomTom Parking Availability 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 TomTom Parking. 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 TomTom Parking Availability MCP in LangChain

LangChain lets you build reasoning chains where the AI decides which tool to call and in what order. You can first search for spots, then check detailed availability metadata, all within a single workflow.

This MCP Server handles location data and real-time availability metadata. Your agent reads this specific data to inform its decisions.

Yes, the client supports multi-server aggregation. This means you can combine results from several MCP tools into one coherent workflow.

Latency depends on your network and the complexity of the tool calls in your chain. Tracing allows you to measure exactly how long each step takes.

You can maintain a session context using `client.session()`. This lets subsequent tool calls reference previous search results or configurations, improving continuity.

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