How to integrate Radar MCP with Pydantic AI

This guide walks you through connecting Radar to Pydantic AI using the Composio tool router. By the end, you'll have a working Radar agent that can autocomplete address based on partial input, get users currently inside geofence, convert address to latitude and longitude through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Radar account through Composio's Radar MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Api Key

Radar is a location infrastructure platform providing APIs and SDKs for geofencing, geocoding, and location tracking. It helps developers add precise, scalable location features to any app with minimal effort.

37 Tools

Introduction

This guide walks you through connecting Radar to Pydantic AI using the Composio tool router. By the end, you'll have a working Radar agent that can autocomplete address based on partial input, get users currently inside geofence, convert address to latitude and longitude through natural language commands.

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

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

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TL;DR

Here's what you'll learn:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Radar
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your Radar workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed for async/await patterns

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

The Radar MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Radar account. It provides structured and secure access to advanced location services, so your agent can perform actions like geocoding addresses, managing geofences, tracking trips, searching places, and retrieving location context on your behalf.

  • Address and place autocomplete: Instantly get relevant address or place suggestions based on partial user input, improving data quality and user experience.
  • Precise geocoding and location context: Convert full addresses to latitude/longitude and fetch rich context—including region, geofence, and place details—for any set of coordinates.
  • Geofence management: Retrieve, create, or delete geofences to define dynamic boundaries and monitor activity within specific areas automatically.
  • Trip creation and tracking: Start, fetch, or delete trips to enable real-time location tracking and trip management for devices or users.
  • Live user monitoring in geofences: Effortlessly list all users currently inside a defined geofence, supporting presence-based automation and analytics.

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

What is Composio SDK?

Composio's Composio SDK 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 Composio SDK

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

How the Composio SDK works

The Composio SDK 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

Step by step09 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account with an active API key
  • Basic familiarity with Python and async programming
2

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.
3

Install dependencies

bash
pip install composio pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Radar
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file
4

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

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs
5

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Radar
  • MCPServerStreamableHTTP connects to the Radar MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant
6

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Radar
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["radar"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to Radar tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
7

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
radar_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[radar_mcp],
    instructions=(
        "You are a Radar assistant. Use Radar tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Radar endpoint
  • The agent uses GPT-5 to interpret user commands and perform Radar operations
  • The instructions field defines the agent's role and behavior
8

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Radar.\n")

while True:
    user_input = input("You: ").strip()
    if user_input.lower() in {"exit", "quit", "bye"}:
        print("\nGoodbye!")
        break
    if not user_input:
        continue

    print("\nAgent is thinking...\n", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Radar API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns
9

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with Radar and Pydantic AI:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Radar
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["radar"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    radar_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[radar_mcp],
        instructions=(
            "You are a Radar assistant. Use Radar tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Radar.\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "bye"}:
            print("\nGoodbye!")
            break
        if not user_input:
            continue

        print("\nAgent is thinking...\n", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've built a Pydantic AI agent that can interact with Radar through Composio's Tool Router. With this setup, your agent can perform real Radar actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Radar for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.
TOOLS

Supported Tools

Every Radar action and event your agent gets out of the box.

Autocomplete Address or Place

Tool to autocomplete partial addresses and place names based on relevance and proximity.

Create Beacon

Tool to create a new beacon in Radar.

Create Trip

Tool to create a new trip.

Delete Beacon

Tool to delete a beacon by its Radar ID.

Delete Geofence

Tool to delete a geofence by ID.

Delete Geofence By Tag

Tool to delete a geofence by tag and external ID.

Delete Trip

Tool to delete a trip by its Radar ID or external ID.

Delete User

Tool to delete a user by Radar _id, userId, or deviceId.

Forward Geocode

Tool to convert an address into geographic coordinates.

Get Beacon

Tool to retrieve a beacon by Radar _id.

Get Beacon By Tag

Tool to get a specific beacon by tag and external ID.

Get Context for Location

Tool to retrieve context for a given location.

Get Geofence

Tool to retrieve a geofence by Radar _id or tag/externalId.

Get Places Settings

Tool to retrieve current Places settings for your Radar project.

Get Route Directions

Tool to get turn-by-turn directions between multiple locations.

Get Route Matrix

Tool to calculate travel distance and duration between multiple origins and destinations for up to 625 routes.

Get Trip

Tool to retrieve a trip by ID or externalId.

Get User

Tool to get a user by Radar _id, userId, or deviceId.

Get Users in Geofence

Tool to retrieve users currently within a specific geofence.

IP Geocode

Tool to geocode an IP address to city, state, and country.

List Events

Tool to list events.

List Geofences

Tool to list all geofences sorted by updated time.

List Trips

Tool to list all trips, sorted by updated time.

List Users

Tool to list Radar users sorted by update time.

Reverse Geocode

Tool to convert geographic coordinates to structured addresses.

Route Distance

Tool to compute distance and travel time between origins and destinations.

Search Geofences

Tool to search for geofences near a given location.

Search Places Near Location

Tool to search for places near given coordinates.

Search Users Near Location

Tool to search for users near a location.

Track Location Update

Tool to track a user's location update.

Update Places Settings

Tool to update Places settings for your Radar project including chain metadata preferences.

Update Trip

Tool to update a trip.

Update Trip By ID

Tool to update a trip status by Radar _id or external ID.

Upsert Beacon by ID

Tool to create or update a beacon by Radar _id.

Upsert Beacon by Tag

Tool to create or update a beacon by tag and externalId.

Upsert Geofence

Tool to create or update a geofence by tag and externalId.

Upsert Geofence By ID

Tool to create or update a geofence by Radar _id.

FAQ

Frequently asked questions

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

Yes, you can. Pydantic AI 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 Radar tools.

Yes, absolutely. You can configure which Radar 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.

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 Radar data and credentials are handled as safely as possible.

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