How to Use the TomTom Parking Availability MCP in CrewAI

Q: What process does TomTom Parking Availability follow when running with CrewAI?

The flow is sequential: an agent checks status (checkapistatus), a second researches spots (searchparkingspots), and the final action agent validates details (getparkingdetails).

Q: Does TomTom Parking Availability require specific data for CrewAI agents to work?

Yes. You must provide coordinates or an address string for searchparkingspots so the research agent has a starting point for its investigation.

Run autonomous TomTom Parking Availability research with CrewAI's specialized agents.

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Connect TomTom Parking Availability MCP to CrewAI

Create your Vinkius account to connect TomTom Parking Availability to CrewAI 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 CrewAI

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Audit Status with MCP Server

Set up a monitoring agent to first call `check_api_status`. This establishes the initial ground truth for your entire operation. The monitor agent checks this status and reports back, letting the subsequent analysis agents know if they can proceed or if they need to escalate an alert.

Research Spots with CrewAI

Assign a research agent the task of finding locations. This agent executes `search_parking_spots` using provided coordinates. The initial list returned by this tool is passed into a second, specialized analysis agent for filtering and prioritization.

Finalize Data with MCP Server

After the research agent finds candidates, assign an action agent to use `get_parking_details`. This finalizes the data by fetching deep metadata for each candidate spot. The resulting structured information is then compiled and presented by a moderator agent to the user.

Setup guide

Set up TomTom Parking Availability MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke TomTom Parking Availability tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="TomTom Parking Availability Analyst",
    goal="Access and analyze TomTom Parking Availability data via MCP.",
    backstory="Expert analyst with direct TomTom Parking Availability access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent TomTom Parking Availability transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="TomTom Parking Availability Analyst",
        goal="Access and analyze TomTom Parking Availability data via MCP.",
        backstory="Expert analyst with direct TomTom Parking Availability access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent TomTom Parking Availability transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

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 CrewAI

You assign specialized roles. One agent searches spots using `search_parking_spots`, and a second agent uses `get_parking_details` to get the final data, all managed by a coordinating crew.

The flow is sequential: an agent checks status (`check_api_status`), a second researches spots (`search_parking_spots`), and the final action agent validates details (`get_parking_details`).

Yes. You must provide coordinates or an address string for `search_parking_spots` so the research agent has a starting point for its investigation.

The data involves location details, availability status, and capacity counts. These are the types of metrics your agents analyze to make recommendations.

The server handles location coordinates, availability metadata, and specific parking IDs. This structured set of information allows the crew to build a complete operational view.

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