How to integrate Kanbanize MCP with Pydantic AI

This guide walks you through connecting Kanbanize to Pydantic AI using the Composio tool router. By the end, you'll have a working Kanbanize agent that can move all 'in progress' cards to 'done', create a kanban card for new feature request, list all overdue tasks in your boards through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Kanbanize account through Composio's Kanbanize MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Kanbanize is a project management tool for Lean and Kanban workflows. Streamline product and engineering team collaboration with visual boards and analytics.

24 Tools

Introduction

This guide walks you through connecting Kanbanize to Pydantic AI using the Composio tool router. By the end, you'll have a working Kanbanize agent that can move all 'in progress' cards to 'done', create a kanban card for new feature request, list all overdue tasks in your boards through natural language commands.

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

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

Also integrate Kanbanize with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

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

The Kanbanize MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Kanbanize account. It provides structured and secure access so your agent can perform Kanbanize operations on your behalf.

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

Add a comment to a card

Tool to add a comment to a Kanbanize card.

Check Board Milestone

Tool to check if a milestone is available on the specified board.

Check User Is Card Watcher

Tool to check if a user is a watcher of a specific card.

Delete Board

Tool to delete a board by its ID.

Delete Card

Tool to delete a card from the Kanbanize board.

Delete Tag

Tool to delete a tag from Kanbanize.

Delete Workflow

Tool to delete a workflow for the specified board.

Get Board Block Reasons

Tool to get a list of block reasons available on a board.

Get Board Card Templates

Tool to retrieve a list of card templates available on a Kanbanize board.

Get Child Cards

Tool to retrieve a list of child cards for a specified parent card.

Get Column

Tool to get the details of a specific column from a Kanbanize board.

Get Columns

Tool to get a list of columns for a specific board in Kanbanize.

Get Custom Fields

Tool to retrieve a list of custom fields from Kanbanize with optional filtering.

Get Stickers

Tool to retrieve a list of stickers with optional filtering by sticker IDs, label, availability, and enabled status.

Get User

Tool to get the details of a specified user in Kanbanize.

Get workflow cycle time columns

Tool to retrieve workflow's cycle time columns from a Kanbanize board.

Get Workspace Data Fields

Tool to retrieve a list of data fields available on a workspace.

Remove Board Block Reason

Tool to make a block reason unavailable on a board.

Remove Child Card

Tool to remove the link between a parent card and a child card.

Set card block reason

Tool to block a Kanbanize card by setting a block reason.

Update Board Sticker

Tool to update the properties of a sticker for the specified board.

Update Data Field Workspaces

Tool to add, update, or remove a data field on one or more workspaces via batch operations.

Update Lane Default Setting

Tool to update the default value of a specific lane setting in Kanbanize.

Update Tag

Tool to update the specified tag in Kanbanize.

FRAMEWORKS

How to build Kanbanize MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Kanbanize MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Kanbanize MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Kanbanize MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Kanbanize MCP with Claude Cowork

Codex logo

Codex

Use Kanbanize MCP with Codex

OpenClaw logo

OpenClaw

Use Kanbanize MCP with OpenClaw

Hermes logo

Hermes

Use Kanbanize MCP with Hermes

CLI logo

CLI

Use Kanbanize MCP with CLI

Google ADK logo

Google ADK

Use Kanbanize MCP with Google ADK

LangChain logo

LangChain

Use Kanbanize MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Kanbanize MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Kanbanize MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Kanbanize MCP with LlamaIndex

CrewAI logo

CrewAI

Use Kanbanize MCP with CrewAI

FAQ

Frequently asked questions

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

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

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