How to integrate Canva MCP with LlamaIndex

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Introduction

This guide walks you through connecting Canva to LlamaIndex 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 LlamaIndex 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:
  • Set your OpenAI and Composio API keys
  • Install LlamaIndex and Composio packages
  • Create a Composio Tool Router session for Canva
  • Connect LlamaIndex to the Canva MCP server
  • Build a Canva-powered agent using LlamaIndex
  • Interact with Canva through natural language

What is LlamaIndex?

LlamaIndex is a data framework for building LLM applications. It provides tools for connecting LLMs to external data sources and services through agents and tools.

Key features include:

  • ReAct Agent: Reasoning and acting pattern for tool-using agents
  • MCP Tools: Native support for Model Context Protocol
  • Context Management: Maintain conversation context across interactions
  • Async Support: Built 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 you begin, make sure you have:
  • Python 3.8/Node 16 or higher installed
  • A Composio account with the API key
  • An OpenAI API key
  • A Canva account and project
  • Basic familiarity with async Python/Typescript

Getting API Keys for OpenAI, Composio, and Canva

OpenAI API key (OPENAI_API_KEY)
  • Go to the OpenAI dashboard
  • Create an API key if you don't have one
  • Assign it to OPENAI_API_KEY in .env
Composio API key and user ID
  • Log into the Composio dashboard
  • Copy your API key from Settings
    • Use this as COMPOSIO_API_KEY
  • Pick a stable user identifier (email or ID)
    • Use this as COMPOSIO_USER_ID

Installing dependencies

pip install composio-llamaindex llama-index llama-index-llms-openai llama-index-tools-mcp python-dotenv

Create a new Python project and install the necessary dependencies:

  • composio-llamaindex: Composio's LlamaIndex integration
  • llama-index: Core LlamaIndex framework
  • llama-index-llms-openai: OpenAI LLM integration
  • llama-index-tools-mcp: MCP client for LlamaIndex
  • python-dotenv: Environment variable management

Set environment variables

bash
OPENAI_API_KEY=your-openai-api-key
COMPOSIO_API_KEY=your-composio-api-key
COMPOSIO_USER_ID=your-user-id

Create a .env file in your project root:

These credentials will be used to:

  • Authenticate with OpenAI's GPT-5 model
  • Connect to Composio's Tool Router
  • Identify your Composio user session for Canva access

Import modules

import asyncio
import os
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()

Create a new file called canva_llamaindex_agent.py and import the required modules:

Key imports:

  • asyncio: For async/await support
  • Composio: Main client for Composio services
  • LlamaIndexProvider: Adapts Composio tools for LlamaIndex
  • ReActAgent: LlamaIndex's reasoning and action agent
  • BasicMCPClient: Connects to MCP endpoints
  • McpToolSpec: Converts MCP tools to LlamaIndex format

Load environment variables and initialize Composio

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set in the environment")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment")

What's happening:

This ensures missing credentials cause early, clear errors before the agent attempts to initialise.

Create a Tool Router session and build the agent function

async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["canva"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")

    description = "An agent that uses Composio Tool Router MCP tools to perform Canva actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Canva actions.
    """
    return ReActAgent(tools=tools, llm=llm, description=description, system_prompt=system_prompt, verbose=True)

What's happening here:

  • We create a Composio client using your API key and configure it with the LlamaIndex provider
  • We then create a tool router MCP session for your user, specifying the toolkits we want to use (in this case, canva)
  • The session returns an MCP HTTP endpoint URL that acts as a gateway to all your configured tools
  • LlamaIndex will connect to this endpoint to dynamically discover and use the available Canva tools.
  • The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent.

Create an interactive chat loop

async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")

What's happening here:

  • We're creating a direct terminal interface to chat with your Canva database
  • The LLM's responses are streamed to the CLI for faster interaction.
  • The agent uses context to maintain conversation history
  • You can type 'quit' or 'exit' to stop the chat loop gracefully
  • Agent responses and any errors are displayed in a clear, readable format

Define the main entry point

async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")

What's happening here:

  • We're orchestrating the entire application flow
  • The agent gets built with proper error handling
  • Then we kick off the interactive chat loop so you can start talking to Canva

Run the agent

npx ts-node llamaindex-agent.ts

When prompted, authenticate and authorise your agent with Canva, then start asking questions.

Complete Code

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

import asyncio
import os
import signal
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")

async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["canva"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")
    description = "An agent that uses Composio Tool Router MCP tools to perform Canva actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Canva actions.
    """
    return ReActAgent(
        tools=tools,
        llm=llm,
        description=description,
        system_prompt=system_prompt,
        verbose=True,
    );

async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")

async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")

Conclusion

You've successfully connected Canva to LlamaIndex through Composio's Tool Router MCP layer. Key takeaways:
  • Tool Router dynamically exposes Canva tools through an MCP endpoint
  • LlamaIndex's ReActAgent handles reasoning and orchestration; Composio handles integrations
  • The agent becomes more capable without increasing prompt size
  • Async Python provides clean, efficient execution of agent workflows
You can easily extend this to other toolkits like Gmail, Notion, Stripe, GitHub, and more by adding them to the toolkits parameter.

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

CrewAI

CrewAI

Use Canva MCP with CrewAI

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 LlamaIndex?

Yes, you can. LlamaIndex 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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HubSpot
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Altera
DataStax
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Context
ASU
Letta
glean
HubSpot
Agent.ai
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Entelligence
Rolai

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