How do I find my TomTom API Key?

Log in to the [**TomTom Developer Portal**](https://developer.tomtom.com/), create an account, and generate a new API Key in your dashboard. Copy and paste it below.

Does it support real-time availability?

Yes. The API provides real-time occupancy data for supported parking facilities where sensor metadata is available.

Can the agent search by city name?

Yes. Use the `search_parking_spots` tool and provide the city name or a general query. Your agent will return matching facilities in that area instantly.

TomTom Parking Availability MCP Server for Pydantic AI 3 tools — connect in under 2 minutes

Name: TomTom Parking Availability
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 3 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect TomTom Parking Availability through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

Get MCP Server for AI Agents

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Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to TomTom Parking Availability "
            "(3 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in TomTom Parking Availability?"
    )
    print(result.data)

asyncio.run(main())

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About TomTom Parking Availability MCP Server

Empower your AI agent to orchestrate your entire urban mobility and parking auditing workflow with TomTom Parking Availability, the comprehensive source for real-time parking data. By connecting the TomTom API to your agent, you transform complex location searches into a natural conversation. Your agent can instantly search for parking facilities, audit address metadata, and retrieve coordinate details without you ever touching a navigation app. Whether you are conducting logistics research or managing regional fleet constraints, your agent acts as a real-time mobility consultant, ensuring your data is always precise and localized.

Pydantic AI validates every TomTom Parking Availability tool response against typed schemas, catching data inconsistencies at build time. Connect 3 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Parking Auditing — Search for thousands of parking spots near a specific location and retrieve detailed metadata, including facility names and addresses.
Location Oversight — Audit the exact geographic coordinates for specific parking facilities to understand local distribution instantly.
Availability Discovery — Query detailed POI information for specific parking IDs to assist in deep-dive urban planning.
Mobility Intelligence — Retrieve high-resolution location details to assist in regional logistics and fleet management.
Operational Monitoring — Check API status to ensure your mobility research workflow is always operational.

The TomTom Parking Availability MCP Server exposes 3 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect TomTom Parking Availability to Pydantic AI via MCP

Follow these steps to integrate the TomTom Parking Availability MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 3 tools from TomTom Parking Availability with type-safe schemas

Why Use Pydantic AI with the TomTom Parking Availability MCP Server

Pydantic AI provides unique advantages when paired with TomTom Parking Availability through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your TomTom Parking Availability integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your TomTom Parking Availability connection logic from agent behavior for testable, maintainable code

TomTom Parking Availability + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the TomTom Parking Availability MCP Server delivers measurable value.

Type-safe data pipelines: query TomTom Parking Availability with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple TomTom Parking Availability tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query TomTom Parking Availability and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock TomTom Parking Availability responses and write comprehensive agent tests

TomTom Parking Availability MCP Tools for Pydantic AI (3)

These 3 tools become available when you connect TomTom Parking Availability to Pydantic AI via MCP:

check_api_status

Check if the TomTom Parking service is operational

get_parking_details

Get full details and availability metadata for a specific parking ID

search_parking_spots

Search for parking spots near a specific location

Example Prompts for TomTom Parking Availability in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with TomTom Parking Availability immediately.

"Search for parking spots in 'San Francisco' using TomTom."

"What is the availability for parking ID '12345'?"

"Find parking near latitude 37.7749 and longitude -122.4194."

Troubleshooting TomTom Parking Availability MCP Server with Pydantic AI

Common issues when connecting TomTom Parking Availability to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

TomTom Parking Availability + Pydantic AI FAQ

Common questions about integrating TomTom Parking Availability MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer — your TomTom Parking Availability MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.