How to integrate Supportbee MCP with Pydantic AI

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Introduction

This guide walks you through connecting Supportbee to Pydantic AI using the Composio tool router. By the end, you'll have a working Supportbee agent that can archive all tickets resolved this week, assign new tickets to the support team, create a reusable snippet for refund replies, reply to the oldest open ticket with a template through natural language commands.

This guide will help you understand how to give your Pydantic AI agent real control over a Supportbee account through Composio's Supportbee 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:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Supportbee
  • 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 Supportbee 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 Supportbee MCP server, and what's possible with it?

The Supportbee MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Supportbee account. It provides structured and secure access to your support ticketing system, so your agent can perform actions like creating and replying to tickets, managing team assignments, organizing tickets, and automating support workflows on your behalf.

  • Automated ticket creation and updates: Instantly open new support tickets, update their content, or post replies to customer inquiries without leaving your workflow.
  • Team assignment and ticket routing: Direct your agent to assign tickets to the right team or agent, ensuring every request is handled by the appropriate group.
  • Archiving and deleting tickets: Keep your helpdesk organized by having the agent archive resolved tickets or permanently remove unwanted ones from the system.
  • Reusable response snippets: Let your agent create, manage, and delete response templates so your team can reply faster and more consistently.
  • Rule-based workflow automation: Empower your agent to create new automation rules that streamline ticket routing, escalation, and handling based on custom conditions.

Supported Tools & Triggers

Tools
Archive SupportBee TicketTool to archive a supportbee ticket by its id.
Assign Ticket to TeamTool to assign a ticket to a team.
Create RuleTool to create a new routing or automation rule in supportbee.
Create SnippetTool to create a reusable snippet for ticket responses.
Create SupportBee TicketTool to create a new support ticket.
Create Ticket ReplyTool to post a reply to a ticket.
Create SupportBee UserTool to create a new user in supportbee.
Delete SnippetTool to delete a snippet by its id.
Delete SupportBee TicketTool to permanently delete a trashed ticket.
Fetch EmailsTool to retrieve all forwarding email addresses for the company.
Fetch SupportBee LabelsTool to retrieve all custom labels.
Fetch SnippetsTool to fetch all saved snippets.
Fetch SupportBee Team by IDTool to fetch a supportbee team by its id.
Fetch SupportBee TeamsTool to retrieve all teams in the company.
Get Avg First Response Time ReportTool to retrieve average first response time data points.
Get Replies Count ReportTool to get replies count data points over time.
Get Tickets Count ReportTool to get ticket count data points over time.
List Ticket CommentsTool to list all comments for a ticket.
List Ticket RepliesTool to list all replies for a specific ticket.
List TicketsTool to list tickets.
Mark SupportBee Ticket as AnsweredTool to mark a ticket as answered.
Mark SupportBee Ticket as SpamTool to mark a supportbee ticket as spam.
Mark SupportBee Ticket as UnansweredTool to mark a ticket as unanswered.
Search SupportBee TicketsTool to search supportbee tickets.
Show Ticket ReplyTool to fetch a specific reply for a supportbee ticket.
Show SupportBee User or Customer GroupTool to retrieve a user or customer group by id.
Trash SupportBee TicketTool to trash a supportbee ticket by its id.
Unarchive SupportBee TicketTool to unarchive a supportbee ticket by its id.
Unassign Ticket from TeamTool to un-assign a ticket from its assigned team.
Unassign User From TicketTool to unassign the user from a ticket.
Unmark SupportBee Ticket as SpamTool to unmark a supportbee ticket as spam.
Untrash SupportBee TicketTool to untrash (restore) a supportbee ticket by its id.
Update SupportBee UserUpdate supportbee user

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 with an active API key
  • Basic familiarity with Python and async programming

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 pydantic-ai python-dotenv

Install the required libraries.

What's happening:

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

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

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 Supportbee
  • MCPServerStreamableHTTP connects to the Supportbee MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant

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 Supportbee
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["supportbee"],
    )
    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 Supportbee 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

Initialize the Pydantic AI Agent

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

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 Supportbee.\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
  • Supportbee API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns

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 Supportbee 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 Supportbee
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["supportbee"],
    )
    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
    supportbee_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[supportbee_mcp],
        instructions=(
            "You are a Supportbee assistant. Use Supportbee 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 Supportbee.\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 Supportbee through Composio's Tool Router. With this setup, your agent can perform real Supportbee 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 + Supportbee for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.

How to build Supportbee MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Supportbee MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Supportbee MCP with Claude Agent SDK

Google ADK

Google ADK

Use Supportbee MCP with Google ADK

LangChain

LangChain

Use Supportbee MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Supportbee MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Supportbee MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Supportbee MCP with LlamaIndex

CrewAI

CrewAI

Use Supportbee MCP with CrewAI

FAQ

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

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

Can I use Tool Router MCP with Pydantic AI?

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 Supportbee tools.

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

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

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