How to integrate Canva MCP with Pydantic AI

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Introduction

This guide walks you through connecting Canva to Pydantic AI using the Composio tool router. By the end, you'll have a working Canva agent that can create a new instagram post design, list my brand templates for social use, start a folder for this project’s assets, reply to comments on a shared design through natural language commands.

This guide will help you understand how to give your Pydantic AI agent real control over a Canva account through Composio's Canva 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 Canva
  • 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 Canva 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 Canva MCP server, and what's possible with it?

The Canva MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Canva account. It provides structured and secure access to your Canva designs, templates, folders, assets, and user details, so your agent can create designs, organize projects, manage assets, and collaborate on feedback for you.

  • Automated design creation and asset integration: Direct your agent to generate new Canva designs using templates or custom dimensions, and add assets from your projects automatically.
  • Seamless folder and project organization: Have the agent create user or subfolders to keep your Canva projects structured and easily accessible.
  • Asset management and cleanup: Let your agent fetch upload statuses, manage, or delete assets by ID, helping you keep your design library up to date.
  • Collaborative design feedback: Empower your agent to add comments or reply within designs, making it easy to facilitate feedback and teamwork directly in Canva.
  • User and team information retrieval: Quickly obtain user or team details, allowing your agent to personalize interactions and automate workflows based on your Canva account info.

Supported Tools & Triggers

Tools
Access user specific brand templates listThis year, brand template ids will change; integrations storing them must update within 6 months.
Create canva design with optional assetCreate a new canva design using a preset or custom dimensions, and add an asset with `asset id` from a user's project using relevant apis.
Create comment reply in designThis preview api allows replying to comments within a design on canva, with a limit of 100 replies per comment.
Create design comment in preview apiThis api is in preview and may change without notice; integrations using it won't pass review.
Create user or sub folderThis api creates a folder in a canva user's projects at the top level or within another folder, returning the new folder's id and additional details upon success.
Delete asset by idYou can delete an asset by specifying its `assetid`.
Exchange oauth 2 0 access or refresh tokenThe oauth 2.
Fetch asset upload job statusSummarize asset upload outcome by repeatedly calling the endpoint until a 'success' or 'failed' status is received after using the create asset upload job api.
Fetch canva connect signing public keysThe api for verifying canva webhooks, 'connect/keys,' is in preview, meaning unstable, not for public integrations, and provides a rotating jwk for signature verification to prevent replay attacks.
Fetch current user detailsReturns the user id, team id, and display name of the user account associated with the provided access token.
Fetch design metadata and access informationGets the metadata for a design.
Get design export job resultGet the outcome of a canva design export job; if done, receive download links for the design’s pages.
Initiate canva design autofill jobUpcoming brand template id updates require migration within 6 months.
Initiates canva design export jobCanva's new job feature exports designs in multiple formats using a design id, with provided download links.
List design pages with paginationPreview api for canva: subject to unannounced changes and not for public integrations.
List folder items by type with sortingLists the items in a folder, including each item's `type`.
List User DesignsProvides a summary of canva user designs, includes search filtering, and allows showing both self-created and shared designs with sorting options.
Move item to specified folderTransfers an item to a different folder by specifying both the destination folder's id and the item's id.
Remove folder and move contents to trashDeletes a folder by moving the user's content to trash and reassigning other users' content to their top-level projects.
Retrieve app public key setReturns the json web key set (public keys) of an app.
Retrieve a specific design commentThis preview api is subject to unannounced changes and can't be used in public integrations.
Retrieve asset metadata by idYou can retrieve the metadata of an asset by specifying its `assetid`.
Retrieve brand template dataset definitionCanva's brand template ids will change later this year, including a 6-month integration migration.
Retrieve canva enterprise brand template metadataUpcoming update will change brand template ids; integrations must migrate within 6 months.
Retrieve design autofill job statusApi users with canva enterprise membership can retrieve design autofill job results, potentially requiring multiple requests until a `success` or `failed` status is received.
Retrieve design import job statusGets the status and results of design import jobs created using the [create design import job api](https://www.
Retrieve folder details by idGets the name and other details of a folder using a folder's `folderid`.
RetrieveuserprofiledataCurrently, this returns the display name of the user account associated with the provided access token.
Revoke oauth tokensRevoke a refresh token to end its lineage and user consent, requiring re-authentication.
Update asset s name and tags by idYou can update the name and tags of an asset by specifying its `assetid`.
Update folder details by idUpdates a folder's details using its `folderid`.
Validate oauth token propertiesCheck an access token's validity and properties via introspection, requiring authentication.

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

OpenAI Agents SDK

OpenAI Agents SDK

Use Canva MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Canva MCP with Claude Agent SDK

Google ADK

Google ADK

Use Canva MCP with Google ADK

LangChain

LangChain

Use Canva MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Canva MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Canva MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Canva MCP with LlamaIndex

CrewAI

CrewAI

Use Canva MCP with CrewAI

Claude Code

Claude Code

Use Canva MCP with Claude Code

FAQ

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

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

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

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

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