How to Use the TomTom MCP in CrewAI

Q: How can TomTom assist with multi-agent routing using CrewAI?

The crew uses calculateroute to establish the initial path and summary. If traffic is a concern, one agent runs gettrafficflowsegment to check segment quality, allowing the team to automatically recommend an alternate route.

Q: If I use TomTom's geocoding with CrewAI, what kind of data does it handle?

The server manages both structuredgeocoding for highly reliable input (like ZIP codes and country codes) and fuzzygeocoding when the user provides vague or misspelled address strings. The crew selects the right tool based on input quality.

Q: Does TomTom's MCP Server support checking real-time traffic with CrewAI?

Absolutely. One agent can call gettrafficincidents using a min/max coordinate bounding box to check for accidents or closures. This live data feeds into the team, allowing them to adjust the final plan immediately.

Q: Can I use TomTom to find local amenities with CrewAI?

Yes. One agent searches using searchpoibycategory, and another takes the resulting ID to call getpoidetails. The collaborative approach ensures you get both a list of candidates and their full metadata.

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Setup TomTom with CrewAI

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Perplexity

Coordinating complex travel analysis

A dedicated agent can use `calculate_route` to determine the best path and summary time. Another specialized agent handles feasibility checks with `calculate_reachable_range`, confirming if the destination is physically possible given a time budget. The crew works together: one researches routes, another analyzes the necessary travel time, making the whole operation autonomous.

Gathering deep Point of Interest data

One agent can use `search_poi_by_category` to find general locations like 'restaurants' near a center coordinate. A second agent then uses `get_poi_details` on the specific ID found, pulling rich metadata like operating hours or reviews. This role-based specialization ensures that you get both the list of candidates and the deep dive details needed for action.

Processing raw location inputs

When input is messy, one agent uses `fuzzy_geocoding` to clean up a partial or misspelled physical address into precise coordinates. This ensures the subsequent agents have reliable starting data. If you only get coordinates but need a formal street name, another specialized agent runs `reverse_geocoding`, turning raw points into usable addresses.

Setup guide

Set up TomTom 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 tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

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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 Analyst",
        goal="Access and analyze TomTom data via MCP.",
        backstory="Expert analyst with direct TomTom access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent TomTom 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. 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 MCP in CrewAI

The crew uses `calculate_route` to establish the initial path and summary. If traffic is a concern, one agent runs `get_traffic_flow_segment` to check segment quality, allowing the team to automatically recommend an alternate route.

The server manages both `structured_geocoding` for highly reliable input (like ZIP codes and country codes) and `fuzzy_geocoding` when the user provides vague or misspelled address strings. The crew selects the right tool based on input quality.

Absolutely. One agent can call `get_traffic_incidents` using a min/max coordinate bounding box to check for accidents or closures. This live data feeds into the team, allowing them to adjust the final plan immediately.

This server manages geographic coordinates, detailed Point of Interest metadata, and real-time traffic flow speed metrics. It handles all necessary spatial data for autonomous operations.

Yes. One agent searches using `search_poi_by_category`, and another takes the resulting ID to call `get_poi_details`. The collaborative approach ensures you get both a list of candidates and their full metadata.

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