How to integrate Detrack MCP with Pydantic AI

This guide walks you through connecting Detrack to Pydantic AI using the Composio tool router. By the end, you'll have a working Detrack agent that can list all deliveries scheduled for today, edit delivery details for a specific order, view all vehicles in your fleet through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Detrack account through Composio's Detrack MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Detrack is a delivery management platform for real-time tracking and proof of delivery. It helps businesses automate notifications and keep customers updated every step of the way.

19 Tools

Introduction

This guide walks you through connecting Detrack to Pydantic AI using the Composio tool router. By the end, you'll have a working Detrack agent that can list all deliveries scheduled for today, edit delivery details for a specific order, view all vehicles in your fleet through natural language commands.

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

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

Also integrate Detrack with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

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 Detrack
  • 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 Detrack 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 Detrack MCP server, and what's possible with it?

The Detrack MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Detrack account. It provides structured and secure access to your delivery management system, so your agent can perform actions like tracking deliveries, managing jobs, editing or deleting jobs, and viewing vehicles on your behalf.

  • Real-time delivery and collection management: Instantly create, edit, or delete delivery and collection jobs, letting your agent keep your schedules and records up to date.
  • Comprehensive job search and filtering: Ask your agent to search for deliveries, collections, or vehicles with flexible criteria—by date, status, country, and more.
  • Bulk actions for efficient operations: Direct your agent to delete all deliveries or collections for a specific date, making large-scale updates a breeze.
  • Fleet visibility and vehicle management: Retrieve a complete list of all your vehicles, so your agent can help with asset tracking and resource planning.
  • Detailed job listing and reporting: Let your agent fetch and summarize all jobs or collections, providing daily overviews and insights for your logistics workflow.

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 Detrack
  • 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 Detrack
  • MCPServerStreamableHTTP connects to the Detrack 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 Detrack
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["detrack"],
    )
    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 Detrack 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
detrack_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[detrack_mcp],
    instructions=(
        "You are a Detrack assistant. Use Detrack tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Detrack endpoint
  • The agent uses GPT-5 to interpret user commands and perform Detrack 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 Detrack.\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
  • Detrack 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 Detrack 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 Detrack
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["detrack"],
    )
    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
    detrack_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[detrack_mcp],
        instructions=(
            "You are a Detrack assistant. Use Detrack 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 Detrack.\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 Detrack through Composio's Tool Router. With this setup, your agent can perform real Detrack 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 + Detrack 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 Detrack action and event your agent gets out of the box.

Add Collection

Add a new collection job in Detrack.

Bulk Create Depots

Tool to create multiple depot locations in one request.

Create Depot

Create a new depot in Detrack.

Delete All Collections

Tool to delete all collections in the account.

Delete All Deliveries

Tool to delete all deliveries for a specific date.

Delete Delivery

Tool to delete one or more deliveries by date and D.

Bulk Delete Depots

Tool to delete multiple depots in a single request.

Delete Job by Query

Tool to delete a job by DO number using query parameters.

Edit Delivery

Edit one or more existing deliveries by date and D.

Get Job by DO and Date

Tool to retrieve a specific job by its DO (Delivery Order) number and date.

Get Job By Query

Tool to retrieve a single job by DO number using query parameters.

List Depots

Tool to list all depot locations with pagination.

List Jobs V2

Tool to list jobs with pagination and filtering using Detrack API v2.

Search

Tool to search for deliveries, collections, or vehicles.

Search Jobs

Search jobs with advanced filters including date range, DO number, statuses, groups, vehicles, zones, and more.

Update Depots Bulk

Update multiple depot locations in a single request.

View All Collections

View all collection jobs scheduled for a specific date in Detrack.

View All Deliveries

Tool to view all deliveries for a specific date.

View All Vehicles

Retrieve all vehicles registered in your Detrack account.

FRAMEWORKS

How to build Detrack MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Detrack MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Detrack MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Detrack MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Detrack MCP with Claude Cowork

Codex logo

Codex

Use Detrack MCP with Codex

OpenClaw logo

OpenClaw

Use Detrack MCP with OpenClaw

Hermes logo

Hermes

Use Detrack MCP with Hermes

CLI logo

CLI

Use Detrack MCP with CLI

Google ADK logo

Google ADK

Use Detrack MCP with Google ADK

LangChain logo

LangChain

Use Detrack MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Detrack MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Detrack MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Detrack MCP with LlamaIndex

CrewAI logo

CrewAI

Use Detrack MCP with CrewAI

FAQ

Frequently asked questions

With a standalone Detrack MCP server, the agents and LLMs can only access a fixed set of Detrack tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Detrack 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 Detrack tools.

Yes, absolutely. You can configure which Detrack 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 Detrack data and credentials are handled as safely as possible.

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