How to integrate Canva MCP with Autogen

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Introduction

This guide walks you through connecting Canva to AutoGen 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 AutoGen 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:
  • Get and set up your OpenAI and Composio API keys
  • Install the required dependencies for Autogen and Composio
  • Initialize Composio and create a Tool Router session for Canva
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Canva tools
  • Run a live chat loop where you ask the agent to perform Canva operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

  • Multi-Agent Systems: Build collaborative agent workflows
  • MCP Workbench: Native support for Model Context Protocol tools
  • Streaming HTTP: Connect to external services through streamable HTTP
  • AssistantAgent: Pre-built agent class for tool-using assistants

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

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Canva account you can connect to Composio
  • Some basic familiarity with Autogen and Python async

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 python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to Canva via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

  • COMPOSIO_API_KEY is required to talk to Composio
  • OPENAI_API_KEY is used by Autogen's OpenAI client
  • USER_ID is how Composio identifies which user's Canva connections to use

Import dependencies and create Tool Router session

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Canva session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["canva"]
    )
    url = session.mcp.url
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes Canva tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to

Configure MCP parameters for Autogen

python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

  • url points to the Tool Router MCP endpoint from Composio
  • timeout is the HTTP timeout for requests
  • sse_read_timeout controls how long to wait when streaming responses
  • terminate_on_close=True cleans up the MCP server process when the workbench is closed

Create the model client and agent

python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Canva assistant agent with MCP tools
    agent = AssistantAgent(
        name="canva_assistant",
        description="An AI assistant that helps with Canva operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the Canva tools from the workbench

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Canva related question or task to the agent.\n")

# Conversation loop
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")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which Canva tools to call via MCP
  • agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
  • Typing exit, quit, or bye ends the loop

Complete Code

Here's the complete code to get you started with Canva and AutoGen:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Canva session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["canva"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Canva assistant agent with MCP tools
        agent = AssistantAgent(
            name="canva_assistant",
            description="An AI assistant that helps with Canva operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

        print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
        print("Ask any Canva related question or task to the agent.\n")

        # Conversation loop
        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")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You now have an Autogen assistant wired into Canva through Composio's Tool Router and MCP. From here you can:
  • Add more toolkits to the toolkits list, for example notion or hubspot
  • Refine the agent description to point it at specific workflows
  • Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Canva, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

How to build Canva MCP Agent with another framework

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 Autogen?

Yes, you can. Autogen 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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Letta
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Altera
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Entelligence
Rolai

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