How to integrate Tomtom MCP with CrewAI

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Introduction

This guide walks you through connecting Tomtom to CrewAI using the Composio tool router. By the end, you'll have a working Tomtom agent that can find nearby ev charging stations with live status, calculate fastest driving route to airport, search for italian restaurants around times square, get current traffic speed on main street through natural language commands.

This guide will help you understand how to give your CrewAI agent real control over a Tomtom account through Composio's Tomtom MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

TL;DR

Here's what you'll learn:
  • Get a Composio API key and configure your Tomtom connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Tomtom
  • Build a conversational loop where your agent can execute Tomtom operations

What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.

Key features include:

  • Agent Roles: Define specialized agents with specific goals and backstories
  • Task Management: Create tasks with clear descriptions and expected outputs
  • Crew Orchestration: Combine agents and tasks into collaborative workflows
  • MCP Integration: Connect to external tools through Model Context Protocol

What is the Tomtom MCP server, and what's possible with it?

The Tomtom MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Tomtom account. It provides structured and secure access to TomTom's advanced mapping, navigation, and location services, so your agent can perform actions like calculating routes, searching for points of interest, retrieving live traffic data, and managing map assets on your behalf.

  • Dynamic route calculation and navigation: Ask your agent to generate driving, walking, or cycling routes with waypoints and real-time traffic considerations to optimize travel plans.
  • Flexible location and place search: Let your agent perform fuzzy searches for addresses or points of interest, or find nearby locations by category such as restaurants, EV charging stations, or landmarks.
  • Real-time traffic flow and road insights: Retrieve up-to-date traffic flow data for specific road segments, helping you monitor congestion, speed trends, and plan detours proactively.
  • EV charging station availability: Check the current status and availability of EV charging stations, making it easy to plan electric vehicle journeys with confidence.
  • Map styling and asset management: Manage map fonts, styles, sprites, and copyrights to customize the look and feel of maps integrated into your applications.

Supported Tools & Triggers

Tools
List Map FontsTool to list available font asset versions for map rendering.
List Assets SpritesTool to list available sprites for a given asset version.
List map stylesTool to list available map styles.
Calculate RouteTool to calculate driving routes.
Category SearchTool to search for points of interest by category.
EV Charging Stations AvailabilityTool to retrieve ev charging station availability info.
Flow Segment DataTool to retrieve traffic flow data for a specific road segment.
Fuzzy SearchTool to perform a fuzzy search for addresses and points of interest.
List Sprite VersionsTool to list available sprite asset versions.
Get Map CopyrightsTool to retrieve copyright information for a specific map tile.
MAP_DISPLAY_RASTER_TILETool to retrieve a raster map tile for specified coordinates and zoom.
Map Display Static ImageTool to fetch a static map snapshot given center coords and zoom.
Map Display WMS GetMapTool to retrieve a map image via wms getmap.
Matrix RoutingTool to calculate travel time and distance matrix between multiple locations.
Nearby SearchTool to find points of interest near a specified location.
Points of Interest SearchTool to search for points of interest by query.
Reverse GeocodeTool to convert geographic coordinates into a human-readable address.
Structured GeocodeTool to convert structured address fields into coordinates.
Traffic IncidentsTool to retrieve detailed traffic incidents within a bounding box.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Tomtom connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.

Install dependencies

bash
pip install composio crewai crewai-tools[mcp] python-dotenv
What's happening:
  • composio connects your agent to Tomtom via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools[mcp] includes MCP helpers
  • python-dotenv loads environment variables from .env

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates with Composio
  • USER_ID scopes the session to your account
  • OPENAI_API_KEY lets CrewAI use your chosen OpenAI model

Import dependencies

python
import os
from composio import Composio
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
import dotenv

dotenv.load_dotenv()

COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • CrewAI classes define agents and tasks, and run the workflow
  • MCPServerHTTP connects the agent to an MCP endpoint
  • Composio will give you a short lived Tomtom MCP URL

Create a Composio Tool Router session for Tomtom

python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["tomtom"])

url = session.mcp.url
What's happening:
  • You create a Tomtom only session through Composio
  • Composio returns an MCP HTTP URL that exposes Tomtom tools

Initialize the MCP Server

python
server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users search the internet effectively",
        backstory="You are a helpful assistant with access to search tools.",
        tools=tools,
        verbose=False,
        max_iter=10,
    )
What's Happening:
  • Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication.
  • MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format.
  • Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools.
  • Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops.
  • Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries.

Create a CLI Chatloop and define the Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")

conversation_context = ""

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Conversation history:\n{conversation_context}\n\n"
            f"Current request: {user_input}"
        ),
        expected_output="A helpful response addressing the user's request",
        agent=agent,
    )

    crew = Crew(agents=[agent], tasks=[task], verbose=False)
    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
What's Happening:
  • Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable.
  • Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean.
  • Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses.
  • Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context.
  • Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response.
  • Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity.

Complete Code

Here's the complete code to get you started with Tomtom and CrewAI:

from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter
from composio import Composio
from dotenv import load_dotenv
import os

load_dotenv()

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in the environment.")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment.")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment.")

# Initialize Composio and create a session
composio = Composio(api_key=COMPOSIO_API_KEY)
session = composio.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["tomtom"],
)
url = session.mcp.url

# Configure LLM
llm = LLM(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY"),
)

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users with internet searches",
        backstory="You are an expert assistant with access to Composio Search tools.",
        tools=tools,
        llm=llm,
        verbose=False,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end.\n")

    conversation_context = ""

    while True:
        user_input = input("You: ").strip()

        if user_input.lower() in ["exit", "quit", "bye"]:
            print("\nGoodbye!")
            break

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Conversation history:\n{conversation_context}\n\n"
                f"Current request: {user_input}"
            ),
            expected_output="A helpful response addressing the user's request",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task], verbose=False)
        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")

Conclusion

You now have a CrewAI agent connected to Tomtom through Composio's Tool Router. The agent can perform Tomtom operations through natural language commands.

Next steps:

  • Add role-specific instructions to customize agent behavior
  • Plug in more toolkits for multi-app workflows
  • Chain tasks for complex multi-step operations

How to build Tomtom MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Tomtom MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Tomtom MCP with Claude Agent SDK

Google ADK

Google ADK

Use Tomtom MCP with Google ADK

LangChain

LangChain

Use Tomtom MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Tomtom MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Tomtom MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Tomtom MCP with LlamaIndex

FAQ

What are the differences in Tool Router MCP and Tomtom MCP?

With a standalone Tomtom MCP server, the agents and LLMs can only access a fixed set of Tomtom tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Tomtom and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with CrewAI?

Yes, you can. CrewAI fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Tomtom tools.

Can I manage the permissions and scopes for Tomtom while using Tool Router?

Yes, absolutely. You can configure which Tomtom scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Tomtom data and credentials are handled as safely as possible.

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