How to integrate Tomtom MCP with OpenAI Agents SDK

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Introduction

This guide walks you through connecting Tomtom to the OpenAI Agents SDK 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 OpenAI Agents SDK 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 and set up your OpenAI and Composio API keys
  • Install the necessary dependencies
  • Initialize Composio and create a Tool Router session for Tomtom
  • Configure an AI agent that can use Tomtom as a tool
  • Run a live chat session where you can ask the agent to perform Tomtom operations

What is open-ai-agents-sdk?

The OpenAI Agents SDK is a lightweight framework for building AI agents that can use tools and maintain conversation state. It provides a simple interface for creating agents with hosted MCP tool support.

Key features include:

  • Hosted MCP Tools: Connect to external services through hosted MCP endpoints
  • SQLite Sessions: Persist conversation history across interactions
  • Simple API: Clean interface with Agent, Runner, and tool configuration
  • Streaming Support: Real-time response streaming for interactive applications

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:
  • Composio API Key and OpenAI API Key
  • Primary know-how of OpenAI Agents SDK
  • A live Tomtom project
  • Some knowledge of Python or Typescript

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

Install dependencies

pip install composio_openai_agents openai-agents python-dotenv

Install the Composio SDK and the OpenAI Agents SDK.

Set up environment variables

bash
OPENAI_API_KEY=sk-...your-api-key
COMPOSIO_API_KEY=your-api-key
USER_ID=composio_user@gmail.com

Create a .env file and add your OpenAI and Composio API keys.

Import dependencies

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession
What's happening:
  • You're importing all necessary libraries.
  • The Composio and OpenAIAgentsProvider classes are imported to connect your OpenAI agent to Composio tools like Tomtom.

Set up the Composio instance

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())
What's happening:
  • load_dotenv() loads your .env file so OPENAI_API_KEY and COMPOSIO_API_KEY are available as environment variables.
  • Creating a Composio instance using the API Key and OpenAIAgentsProvider class.

Create a Tool Router session

# Create a Tomtom Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["tomtom"]
)

mcp_url = session.mcp.url

What is happening:

  • You give the Tool Router the user id and the toolkits you want available. Here, it is only tomtom.
  • The router checks the user's Tomtom connection and prepares the MCP endpoint.
  • The returned session.mcp.url is the MCP URL that your agent will use to access Tomtom.
  • This approach keeps things lightweight and lets the agent request Tomtom tools only when needed during the conversation.

Configure the agent

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Tomtom. "
        "Help users perform Tomtom operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)
What's happening:
  • We're creating an Agent instance with a name, model (gpt-5), and clear instructions about its purpose.
  • The agent's instructions tell it that it can access Tomtom and help with queries, inserts, updates, authentication, and fetching database information.
  • The tools array includes a HostedMCPTool that connects to the MCP server URL we created earlier.
  • The headers dict includes the Composio API key for secure authentication with the MCP server.
  • require_approval: 'never' means the agent can execute Tomtom operations without asking for permission each time, making interactions smoother.

Start chat loop and handle conversation

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
What's happening:
  • The program prints a session URL that you visit to authorize Tomtom.
  • After authorization, the chat begins.
  • Each message you type is processed by the agent using Runner.run().
  • The responses are printed to the console, and conversations are saved locally using SQLite.
  • Typing exit, quit, or q cleanly ends the chat.

Complete Code

Here's the complete code to get you started with Tomtom and open-ai-agents-sdk:

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())

# Create Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["tomtom"]
)
mcp_url = session.mcp.url

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Tomtom. "
        "Help users perform Tomtom operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())

Conclusion

This was a starter code for integrating Tomtom MCP with OpenAI Agents SDK to build a functional AI agent that can interact with Tomtom.

Key features:

  • Hosted MCP tool integration through Composio's Tool Router
  • SQLite session persistence for conversation history
  • Simple async chat loop for interactive testing
You can extend this by adding more toolkits, implementing custom business logic, or building a web interface around the agent.

How to build Tomtom MCP Agent with another framework

Claude Agent SDK

Claude Agent SDK

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Google ADK

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LangChain

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Vercel AI SDK

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Use Tomtom MCP with Vercel AI SDK

Mastra AI

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LlamaIndex

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Use Tomtom MCP with LlamaIndex

CrewAI

CrewAI

Use Tomtom MCP with CrewAI

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 OpenAI Agents SDK?

Yes, you can. OpenAI Agents SDK 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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