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How to integrate Supportivekoala MCP with Pydantic AI

Connect Pydantic AI to Supportivekoala MCP. Create a personalized welcome image for new users, generate event flyers using your saved template, and more using natural language, with authentication handled for you.

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Api Key

Supportivekoala is a platform for automating image generation with customizable templates. It makes creating rich, branded visual content programmatically fast and simple.

6 Tools

Introduction

This guide walks you through connecting Supportivekoala to Pydantic AI using the Composio tool router. By the end, you'll have a working Supportivekoala agent that can generate images from templates, create templates, list your images, and fetch template details through natural language commands.

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

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

Also integrate Supportivekoala with

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

The Supportivekoala MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Supportivekoala account. It provides structured and secure access to your image generation workflows, so your agent can create images, manage templates, retrieve image lists, and automate visual content creation entirely on your behalf.

  • Automated image creation using templates: Instantly generate custom images by selecting and modifying templates with dynamic data, all through your agent.
  • Template management and customization: Let your agent create new templates for recurring graphic needs or update existing ones to fit fresh branding and messaging.
  • Image and asset retrieval: Effortlessly list all images generated by your account, allowing your agent to fetch, manage, or reference previous visual assets as needed.
  • Template discovery and details: Retrieve and review all available templates, or fetch full details for any template using its ID—perfect for scaling up campaigns or personalizing content.
  • User registration automation: Seamlessly create new user accounts so your workflows can onboard new teammates or clients without manual intervention.

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

Create Image

Tool to create a new image based on a template.

Create Template

Tool to create a new template for image generation.

List Images

Tool to list images associated with the authenticated user.

List Templates

Tool to retrieve all templates.

Register User

Tool to register a new user account.

Retrieve Template by ID

Tool to retrieve a template by ID.

FRAMEWORKS

How to build Supportivekoala MCP Agent with another framework

Claude Code logo

Claude Code

Use Supportivekoala MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Supportivekoala MCP with Claude Cowork

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Supportivekoala MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Supportivekoala MCP with Claude Agent SDK

Grok Bot logo

Grok Bot

Use Supportivekoala MCP with Grok Bot

Codex logo

Codex

Use Supportivekoala MCP with Codex

OpenClaw logo

OpenClaw

Use Supportivekoala MCP with OpenClaw

Hermes logo

Hermes

Use Supportivekoala MCP with Hermes

Atomic Agent logo

Atomic Agent

Use Supportivekoala MCP with Atomic Agent

Google ADK logo

Google ADK

Use Supportivekoala MCP with Google ADK

LangChain logo

LangChain

Use Supportivekoala MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Supportivekoala MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Supportivekoala MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Supportivekoala MCP with LlamaIndex

CrewAI logo

CrewAI

Use Supportivekoala MCP with CrewAI

AutoGen logo

AutoGen

Use Supportivekoala MCP with AutoGen

FAQ

Frequently asked questions

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

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

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