# How to integrate Kanbanize MCP with LangChain

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

## Introduction

This guide walks you through connecting Kanbanize to LangChain 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 LangChain 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)
- [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:
- Get and set up your OpenAI and Composio API keys
- Connect your Kanbanize project to Composio
- Create a Tool Router MCP session for Kanbanize
- Initialize an MCP client and retrieve Kanbanize tools
- Build a LangChain agent that can interact with Kanbanize
- Set up an interactive chat interface for testing

## What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.
Key features include:
- Agent Framework: Build agents that can use tools and make decisions
- MCP Integration: Connect to external services through Model Context Protocol adapters
- Memory Management: Maintain conversation history across interactions
- Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

## 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

No description provided.

### 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

No description provided.
```python
pip install composio-langchain langchain-mcp-adapters langchain python-dotenv
```

```typescript
npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv
```

### 3. Set up environment variables

Create a .env file in your project root.
What's happening:
- COMPOSIO_API_KEY authenticates your requests to Composio's API
- COMPOSIO_USER_ID identifies the user for session management
- OPENAI_API_KEY enables access to OpenAI's language models
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_user_id_here
OPENAI_API_KEY=your_openai_api_key_here
```

### 4. Import dependencies

No description provided.
```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
```

```typescript
import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

dotenv.config();
```

### 5. Initialize Composio client

What's happening:
- We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
- Creating a Composio instance that will manage our connection to Kanbanize tools
- Validating that COMPOSIO_USER_ID is also set before proceeding
```python
async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
```

```typescript
const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });
```

### 6. 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
- This approach allows the agent to dynamically load and use Kanbanize tools as needed
```python
# Create Tool Router session for Kanbanize
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['kanbanize']
)

url = session.mcp.url
```

```typescript
const session = await composio.create(
    userId as string,
    {
        toolkits: ['kanbanize']
    }
);

const url = session.mcp.url;
```

### 7. Configure the agent with the MCP URL

No description provided.
```python
client = MultiServerMCPClient({
    "kanbanize-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
```

```typescript
const client = new MultiServerMCPClient({
    "kanbanize-agent": {
        transport: "http",
        url: url,
        headers: {
            "x-api-key": process.env.COMPOSIO_API_KEY
        }
    }
});

const tools = await client.getTools();

const agent = createAgent({ model: "gpt-5", tools });
```

### 8. Set up interactive chat interface

No description provided.
```python
conversation_history = []

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

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ['exit', 'quit', 'bye']:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
```

```typescript
let conversationHistory: any[] = [];

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

const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: '
});

rl.prompt();

rl.on('line', async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
        console.log("\nGoodbye!");
        rl.close();
        process.exit(0);
    }

    if (!trimmedInput) {
        rl.prompt();
        return;
    }

    conversationHistory.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    const response = await agent.invoke({ messages: conversationHistory });
    conversationHistory = response.messages;

    const finalResponse = response.messages[response.messages.length - 1]?.content;
    console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\n👋 Session ended.');
        process.exit(0);
    });
```

### 9. Run the application

No description provided.
```python
if __name__ == "__main__":
    asyncio.run(main())
```

```typescript
main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
```

## Complete Code

```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['kanbanize']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "kanbanize-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Kanbanize related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

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

```typescript
import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";  
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

    const session = await composio.create(
        userId as string,
        {
            toolkits: ['kanbanize']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "kanbanize-agent": {
            transport: "http",
            url: url,
            headers: {
                "x-api-key": process.env.COMPOSIO_API_KEY
            }
        }
    });
    
    const tools = await client.getTools();
  
    const agent = createAgent({ model: "gpt-5", tools });
    
    let conversationHistory: any[] = [];
    
    console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
    console.log("Ask any Kanbanize related question or task to the agent.\n");
    
    const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
        prompt: 'You: '
    });

    rl.prompt();

    rl.on('line', async (userInput: string) => {
        const trimmedInput = userInput.trim();
        
        if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
            console.log("\nGoodbye!");
            rl.close();
            process.exit(0);
        }
        
        if (!trimmedInput) {
            rl.prompt();
            return;
        }
        
        conversationHistory.push({ role: "user", content: trimmedInput });
        console.log("\nAgent is thinking...\n");
        
        const response = await agent.invoke({ messages: conversationHistory });
        conversationHistory = response.messages;
        
        const finalResponse = response.messages[response.messages.length - 1]?.content;
        console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\nSession ended.');
        process.exit(0);
    });
}

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
```

## Conclusion

You've successfully built a LangChain agent that can interact with Kanbanize through Composio's Tool Router.
Key features of this implementation:
- Dynamic tool loading through Composio's Tool Router
- Conversation history maintenance for context-aware responses
- Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

## 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)
- [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 LangChain?

Yes, you can. LangChain 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.

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