# How to integrate Kanbanize MCP with Pydantic AI

```json
{
  "title": "How to integrate Kanbanize MCP with Pydantic AI",
  "toolkit": "Kanbanize",
  "toolkit_slug": "kanbanize",
  "framework": "Pydantic AI",
  "framework_slug": "pydantic-ai",
  "url": "https://composio.dev/toolkits/kanbanize/framework/pydantic-ai",
  "markdown_url": "https://composio.dev/toolkits/kanbanize/framework/pydantic-ai.md",
  "updated_at": "2026-03-29T06:39:22.856Z"
}
```

## 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 SDK](https://composio.dev/toolkits/kanbanize/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/kanbanize/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/kanbanize/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/kanbanize/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/kanbanize/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/kanbanize/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/kanbanize/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/kanbanize/framework/cli)
- [Google ADK](https://composio.dev/toolkits/kanbanize/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/kanbanize/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/kanbanize/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/kanbanize/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/kanbanize/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/kanbanize/framework/crew-ai)

## 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.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `KANBANIZE_ADD_CARD_COMMENT` | Add a comment to a card | Tool to add a comment to a Kanbanize card. Use when you need to add notes, updates, or any text-based information to an existing card. |
| `KANBANIZE_CHECK_BOARD_MILESTONE` | Check Board Milestone | Tool to check if a milestone is available on the specified board. Use when you need to verify milestone existence on a specific board. Returns True if the milestone is available (HTTP 204), False if not found (HTTP 404). |
| `KANBANIZE_CHECK_USER_IS_CARD_WATCHER` | Check User Is Card Watcher | Tool to check if a user is a watcher of a specific card. Use when you need to verify if a user is watching a card. |
| `KANBANIZE_DELETE_BOARD` | Delete Board | Tool to delete a board by its ID. Use when you need to permanently remove a board from Kanbanize. Note: The board must be archived before deletion. |
| `KANBANIZE_DELETE_CARD` | Delete Card | Tool to delete a card from the Kanbanize board. Use when you need to permanently remove a card and all its associated data from the board. |
| `KANBANIZE_DELETE_TAG` | Delete Tag | Tool to delete a tag from Kanbanize. Use when removing a tag and optionally replacing it with another tag for all affected cards. |
| `KANBANIZE_DELETE_WORKFLOW` | Delete Workflow | Tool to delete a workflow for the specified board. Use when you need to permanently remove a workflow from a board. |
| `KANBANIZE_GET_BOARD_BLOCK_REASONS` | Get Board Block Reasons | Tool to get a list of block reasons available on a board. Use when you need to retrieve available block reasons for a specific board. |
| `KANBANIZE_GET_BOARD_CARD_TEMPLATES` | Get Board Card Templates | Tool to retrieve a list of card templates available on a Kanbanize board. Use when you need to see what card templates are configured for a specific board. |
| `KANBANIZE_GET_CHILD_CARDS` | Get Child Cards | Tool to retrieve a list of child cards for a specified parent card. Use when you need to view all cards that are children of a given parent card in the Kanbanize hierarchy. |
| `KANBANIZE_GET_COLUMN` | Get Column | Tool to get the details of a specific column from a Kanbanize board. Use when you need to retrieve column information such as name, WIP limit, card ordering, or workflow configuration. |
| `KANBANIZE_GET_COLUMNS` | Get Columns | Tool to get a list of columns for a specific board in Kanbanize. Use when you need to retrieve all columns configured for a board, including their workflow assignments, positions, limits, and display settings. |
| `KANBANIZE_GET_CUSTOM_FIELDS` | Get Custom Fields | Tool to retrieve a list of custom fields from Kanbanize with optional filtering. Use when you need to fetch custom field definitions, filter by field IDs, name, availability level, enabled status, types, or retrieve additional details like boards, card counts, and business rules. |
| `KANBANIZE_GET_STICKERS` | Get Stickers | Tool to retrieve a list of stickers with optional filtering by sticker IDs, label, availability, and enabled status. Use when you need to fetch stickers from Kanbanize to apply to cards or to view available stickers in the system. |
| `KANBANIZE_GET_USER` | Get User | Tool to get the details of a specified user in Kanbanize. Use when you need to retrieve information about a user such as their username, email, real name, avatar, enabled status, language preferences, timezone, and other attributes. |
| `KANBANIZE_GET_WORKFLOW_CYCLE_TIME_COLUMNS` | Get workflow cycle time columns | Tool to retrieve workflow's cycle time columns from a Kanbanize board. Use when you need to identify which columns are included in cycle time calculations for a specific workflow. |
| `KANBANIZE_GET_WORKSPACE_DATA_FIELDS` | Get Workspace Data Fields | Tool to retrieve a list of data fields available on a workspace. Use when you need to fetch all custom data fields configured for a specific Kanbanize workspace. |
| `KANBANIZE_REMOVE_BOARD_BLOCK_REASON` | Remove Board Block Reason | Tool to make a block reason unavailable on a board. Use when you need to remove a specific block reason from a board's available options. |
| `KANBANIZE_REMOVE_CHILD_CARD` | Remove Child Card | Tool to remove the link between a parent card and a child card. Use when you need to unlink a child card from its parent card in Kanbanize. |
| `KANBANIZE_SET_CARD_BLOCK_REASON` | Set card block reason | Tool to block a Kanbanize card by setting a block reason. Use when you need to mark a card as blocked and specify the reason preventing progress. |
| `KANBANIZE_UPDATE_BOARD_STICKER` | Update Board Sticker | Tool to update the properties of a sticker for the specified board. Use when you need to modify usage limits for a sticker on a board or card. |
| `KANBANIZE_UPDATE_DATA_FIELD_WORKSPACES` | Update Data Field Workspaces | Tool to add, update, or remove a data field on one or more workspaces via batch operations. Use when you need to configure data field availability and settings across multiple workspaces. |
| `KANBANIZE_UPDATE_LANE_DEFAULT_SETTING` | Update Lane Default Setting | Tool to update the default value of a specific lane setting in Kanbanize. Use when you need to modify default settings for a lane on a board. |
| `KANBANIZE_UPDATE_TAG` | Update Tag | Tool to update the specified tag in Kanbanize. Use when you need to modify tag properties like label, color, availability, or enabled status. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Kanbanize MCP server is an implementation of the Model Context Protocol that connects your AI agent to Kanbanize. It provides structured and secure access so your agent can perform Kanbanize operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 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

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

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
```bash
pip install composio pydantic-ai python-dotenv
```

### 3. Set up environment variables

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
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key
```

### 4. Import dependencies

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
```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()
```

### 5. Create a Tool Router Session

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
```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")
```

### 6. Initialize the Pydantic AI Agent

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
```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."
    ),
)
```

### 7. Build the chat interface

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
```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()
```

### 8. Run the application

What's happening:
- The asyncio loop launches the agent and keeps it running until you exit
```python
if __name__ == "__main__":
    asyncio.run(main())
```

## Complete Code

```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.

## How to build Kanbanize MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/kanbanize/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/kanbanize/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/kanbanize/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/kanbanize/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/kanbanize/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/kanbanize/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/kanbanize/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/kanbanize/framework/cli)
- [Google ADK](https://composio.dev/toolkits/kanbanize/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/kanbanize/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/kanbanize/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/kanbanize/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/kanbanize/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/kanbanize/framework/crew-ai)

## Related Toolkits

- [Google Sheets](https://composio.dev/toolkits/googlesheets) - Google Sheets is a cloud-based spreadsheet tool for real-time collaboration and data analysis. It lets teams work together from anywhere, updating information instantly.
- [Notion](https://composio.dev/toolkits/notion) - Notion is a collaborative workspace for notes, docs, wikis, and tasks. It streamlines team knowledge, project tracking, and workflow customization in one place.
- [Airtable](https://composio.dev/toolkits/airtable) - Airtable combines the flexibility of spreadsheets with the power of a database for easy project and data management. Teams use Airtable to organize, track, and collaborate with custom views and automations.
- [Asana](https://composio.dev/toolkits/asana) - Asana is a collaborative work management platform for teams to organize and track projects. It streamlines teamwork, boosts productivity, and keeps everyone aligned on goals.
- [Google Tasks](https://composio.dev/toolkits/googletasks) - Google Tasks is a to-do list and task management tool integrated into Gmail and Google Calendar. It helps you organize, track, and complete tasks across your Google ecosystem.
- [Linear](https://composio.dev/toolkits/linear) - Linear is a modern issue tracking and project planning tool for fast-moving teams. It helps streamline workflows, organize projects, and boost productivity.
- [Jira](https://composio.dev/toolkits/jira) - Jira is Atlassian’s platform for bug tracking, issue tracking, and agile project management. It helps teams organize work, prioritize tasks, and deliver projects efficiently.
- [Clickup](https://composio.dev/toolkits/clickup) - ClickUp is an all-in-one productivity platform for managing tasks, docs, goals, and team collaboration. It streamlines project workflows so teams can work smarter and stay organized in one place.
- [Monday](https://composio.dev/toolkits/monday) - Monday.com is a customizable work management platform for project planning and collaboration. It helps teams organize tasks, automate workflows, and track progress in real time.
- [Addressfinder](https://composio.dev/toolkits/addressfinder) - Addressfinder is a data quality platform for verifying addresses, emails, and phone numbers. It helps you ensure accurate customer and contact data every time.
- [Agiled](https://composio.dev/toolkits/agiled) - Agiled is an all-in-one business management platform for CRM, projects, and finance. It helps you streamline workflows, consolidate client data, and manage business processes in one place.
- [Ascora](https://composio.dev/toolkits/ascora) - Ascora is a cloud-based field service management platform for service businesses. It streamlines scheduling, invoicing, and customer operations in one place.
- [Basecamp](https://composio.dev/toolkits/basecamp) - Basecamp is a project management and team collaboration tool by 37signals. It helps teams organize tasks, share files, and communicate efficiently in one place.
- [Beeminder](https://composio.dev/toolkits/beeminder) - Beeminder is an online goal-tracking platform that uses monetary pledges to keep you motivated. Stay accountable and hit your targets with real financial incentives.
- [Boxhero](https://composio.dev/toolkits/boxhero) - Boxhero is a cloud-based inventory management platform for SMBs, offering real-time updates, barcode scanning, and team collaboration. It helps businesses streamline stock tracking and analytics for smarter inventory decisions.
- [Breathe HR](https://composio.dev/toolkits/breathehr) - Breathe HR is cloud-based HR software for SMEs to manage employee data, absences, and performance. It simplifies HR admin, making it easy to keep employee records accurate and up to date.
- [Breeze](https://composio.dev/toolkits/breeze) - Breeze is a project management platform designed to help teams plan, track, and collaborate on projects. It streamlines workflows and keeps everyone on the same page.
- [Bugherd](https://composio.dev/toolkits/bugherd) - Bugherd is a visual feedback and bug tracking tool for websites. It helps teams and clients report website issues directly on live sites for faster fixes.
- [Canny](https://composio.dev/toolkits/canny) - Canny is a platform for managing customer feedback and feature requests. It helps teams prioritize product decisions based on real user insights.
- [Chmeetings](https://composio.dev/toolkits/chmeetings) - Chmeetings is a church management platform for events, members, donations, and volunteers. It streamlines church operations and improves community engagement.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Kanbanize MCP?

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.

### 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 Kanbanize tools.

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

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.

### 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 Kanbanize data and credentials are handled as safely as possible.

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)
