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How to Use the TomTom Parking Availability MCP in Google ADK

Run Enterprise TomTom Parking Availability Agents on Google ADK for BigQuery Integration.

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Google ADK

Connect TomTom Parking Availability MCP to Google ADK

Create your Vinkius account to connect TomTom Parking Availability to Google ADK 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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Verify Service Status

Start by running `check_api_status` to confirm the operational status of the service. For enterprise agents, this initial check is crucial; it prevents unnecessary calls when infrastructure isn't ready. The Google ADK passes these results directly into Gemini models for reasoning. Your agent can flag issues and halt processing before committing resources.

Retrieve Full Spot Data

`get_parking_details` pulls all available metadata for a specific parking ID. Because you're using the Google ADK, this data can be immediately fed into BigQuery alongside your existing enterprise datasets. You don't just get raw text; you get structured fields ready to join with other records in your cloud infrastructure.

Search by Location

The `search_parking_spots` tool finds available parking near a given location. This mechanism outputs clean, query-ready data that Gemini can interpret. The agent handles the search, then uses its long context window to compare those results against millions of tokens worth of historical or related enterprise records.

Setup guide

Set up TomTom Parking Availability MCP in Google ADK

Prerequisites

  • Python 3.10+ installed
  • google-adk package (pip install google-adk)
  • Active Vinkius subscription with a valid endpoint token
  1. 1

    Install Google ADK

    Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.

  2. 2

    Connect via SSE transport

    Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.

  3. 3

    Create an LlmAgent

    Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.

  4. 4

    Run with any Gemini model

    The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with TomTom Parking Availability tools in your ADK agent.

agent.py
from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="TomTom Parking Availability_agent",
    model="gemini-2.0-flash",
    instruction="You have access to TomTom Parking Availability tools via MCP.",
    tools=mcp_tools,
)

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 Google ADK

Your agent uses the MCP tools within the Gemini ecosystem. It runs searches, gets structured data, and then utilizes BigQuery access to correlate that parking information with your other enterprise records.
The server provides location metadata, including IDs and coordinates, along with availability status. This structured output is designed to integrate natively into BigQuery tables for long-term analysis.
Yes. You can pipe the results of `search_parking_spots` through your LlmAgent, allowing it to enrich and validate location findings before committing them to a dataset.
Call `get_parking_details` with the ID. The returned data is highly structured, making it easy for Gemini to ingest and analyze alongside other large-scale context.
You can always start by calling `check_api_status`. This ensures your agents don't waste time on searches if the underlying service is down, saving you compute costs.

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