How to integrate Starshipit MCP with Pydantic AI

This guide walks you through connecting Starshipit to Pydantic AI using the Composio tool router. By the end, you'll have a working Starshipit agent that can create your orders, compare shipping rates, create labels, track parcels, and manage manifests through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Starshipit account through Composio's Starshipit MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Starshipit is a shipping and fulfilment platform for ecommerce orders, labels, tracking, and warehouse operations. It helps teams automate delivery workflows across carriers and sales channels.

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Introduction

This guide walks you through connecting Starshipit to Pydantic AI using the Composio tool router. By the end, you'll have a working Starshipit agent that can create your orders, compare shipping rates, create labels, track parcels, and manage manifests through natural language commands.

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

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

Also integrate Starshipit 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 Starshipit
  • 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 Starshipit 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 Starshipit MCP server, and what's possible with it?

The Starshipit MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Starshipit account. It provides structured and secure access to your orders, shipments, tracking, inventory, and warehouse data, so your agent can create orders, quote shipping rates, generate labels, track deliveries, and review fulfilment performance on your behalf.

  • Order creation and management: Have your agent create unshipped orders, find existing orders, update editable order details, and review orders waiting for fulfilment.
  • Shipping rates and labels: Let the agent compare available shipping services and prices, then generate labels for one order or multiple orders.
  • Tracking and manifests: Direct your agent to check shipment statuses and carrier events, create finalized manifest documents, or review recent manifests.
  • Inventory and product visibility: Instruct your agent to search the product catalogue and review warehouse stock, reserved quantities, and available inventory by product or location.
  • Warehouse performance insights: Have your agent report on recent order cycle times and fulfilment SLA performance across the past 1 to 90 days.

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

Create Manifest

Create and finalize a Starshipit manifest PDF for only the explicitly listed shipments.

Create Order

Create one unshipped Starshipit order with destination, line items, packages, sender, and customs details.

Create Shipping Label

Create shipping labels for one Starshipit order.

Create Shipping Labels

Create shipping labels for multiple existing Starshipit orders in one irreversible batch.

Get Order

Retrieve one order by its Starshipit numeric ID or source-platform order number.

Get Shipping Rates

Quote available Starshipit shipping services and prices for a destination and one or more packages.

Get Tracking

Get current shipment status and carrier tracking events by tracking number or order number.

Get WMS Performance

Return Starshipit WMS order-cycle and SLA performance metrics for the previous 1 to 90 days.

List Manifests

Return one page of manifests in reverse chronological order.

List Products

Search and page through the Starshipit product catalogue with optional sorting.

List Tags

Return one page of user-defined order tags, optionally filtered by tag name.

List Unshipped Orders

Return one page of unshipped orders, optionally limited to orders created or updated after a UTC timestamp.

List WMS Inventory

Return one page of Starshipit WMS inventory grouped by product or location, including stock, reservation, and availability quantities.

Search Orders

Search orders by a phrase and optional field, status, or child-account inclusion filter.

Update Order

Update editable fields on one Starshipit order identified by order_id or order_number.

FRAMEWORKS

How to build Starshipit MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Starshipit MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Starshipit MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Starshipit MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Starshipit MCP with Claude Cowork

Codex logo

Codex

Use Starshipit MCP with Codex

OpenClaw logo

OpenClaw

Use Starshipit MCP with OpenClaw

Hermes logo

Hermes

Use Starshipit MCP with Hermes

CLI logo

CLI

Use Starshipit MCP with CLI

Google ADK logo

Google ADK

Use Starshipit MCP with Google ADK

LangChain logo

LangChain

Use Starshipit MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Starshipit MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Starshipit MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Starshipit MCP with LlamaIndex

CrewAI logo

CrewAI

Use Starshipit MCP with CrewAI

FAQ

Frequently asked questions

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

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

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