How to integrate Kaggle MCP with Autogen

This guide walks you through connecting Kaggle to AutoGen using the Composio tool router. By the end, you'll have a working Kaggle agent that can download data files for the titanic competition, create a new version of your covid-19 dataset, check processing status of your uploaded dataset through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Kaggle account through Composio's Kaggle MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

Get started for free Get a demo

Kaggle

Api Key

Kaggle is a platform for data science and machine learning competitions, datasets, and collaborative notebooks. It makes it easy to find data, participate in challenges, and share insights with a global data community.

35 Tools

Managed auth

Connect Kaggle without auth hassles

We manage OAuth, API keys, token refresh, and scopes — you just build.

Try for free

Introduction

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

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

Also integrate Kaggle with

ChatGPT OpenAI Agents SDK Claude Agent SDK Claude Code Claude Cowork Codex OpenCode Cursor VS Code OpenClaw Hermes CLI Google ADK LangChain Vercel AI SDK Mastra AI LlamaIndex CrewAI

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 Kaggle
Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
Configure an Autogen AssistantAgent that can call Kaggle tools
Run a live chat loop where you ask the agent to perform Kaggle 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 Kaggle MCP server, and what's possible with it?

The Kaggle MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Kaggle account. It provides structured and secure access to your Kaggle datasets, competitions, and configurations, so your agent can perform actions like downloading competition data, creating datasets, submitting entries, and managing dataset versions on your behalf.

Competition data access and download: Let your agent fetch and download competition datasets quickly by specifying a competition ID, so you always have the latest files for analysis.
Automated dataset creation and management: Have your agent create new Kaggle datasets, update metadata, and publish new dataset versions seamlessly, streamlining the process of sharing your work with the community.
Competition entry submission: Empower your agent to submit competition entries automatically once your solution is ready and uploaded, helping you participate in challenges without manual hassle.
Configuration management and setup: Allow your agent to initialize, locate, and update Kaggle API configuration files and keys, ensuring smooth and authenticated operations every time.
Dataset status monitoring: Ask your agent to check the status of uploaded datasets or processing jobs, so you always know when your data is ready for use or public sharing.

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:

Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step08 STEPS

Prerequisites

You will need:

A Composio API key
An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
A Kaggle 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 Kaggle 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 Kaggle 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 Kaggle session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["kaggle"]
    )
    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 Kaggle 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 Kaggle assistant agent with MCP tools
    agent = AssistantAgent(
        name="kaggle_assistant",
        description="An AI assistant that helps with Kaggle 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 Kaggle 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 Kaggle 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 Kaggle 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 Kaggle 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 Kaggle session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["kaggle"]
    )
    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 Kaggle assistant agent with MCP tools
        agent = AssistantAgent(
            name="kaggle_assistant",
            description="An AI assistant that helps with Kaggle 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 Kaggle 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 Kaggle 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 Kaggle, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

TOOLS

Supported Tools

Every Kaggle action and event your agent gets out of the box.

Download competition data files

Downloads all data files for a Kaggle competition as a single zip archive.

Submit Competition Entry

Submit an entry to a Kaggle competition using a previously uploaded file.

Get Kaggle Config Directory

Tool to retrieve the directory of the Kaggle API configuration file.

Initialize Kaggle Configuration

Initialize Kaggle API client configuration.

List Kaggle Configuration Keys

Tool to list local Kaggle API configuration keys.

Get Kaggle Config Path

Tool to retrieve local Kaggle API configuration file path.

Reset Kaggle Configuration

Tool to reset local Kaggle CLI configuration to defaults.

Set Kaggle Configuration

Tool to set a Kaggle CLI configuration parameter.

Unset Kaggle Configuration

Tool to unset a Kaggle CLI configuration parameter.

View Kaggle Configuration

View local Kaggle API credentials and configuration settings.

Dataset Create

Create a new Kaggle dataset with metadata.

Kaggle Dataset Init

Tool to initialize a dataset-metadata.

List Kaggle Dataset Files

Tool to list files in a Kaggle dataset.

Get Dataset Status

Check the processing status of a Kaggle dataset after creation or version update.

Create Dataset Version

Create a new version of an existing Kaggle dataset.

Download competition file

Tool to download a specific data file from a Kaggle competition.

Download competition leaderboard

Tool to download the entire competition leaderboard as a CSV file packaged in a ZIP archive.

Download Kaggle Dataset

Tool to download all files from a Kaggle dataset as a zip archive.

Download Kaggle Dataset File

Tool to download a specific file from a Kaggle dataset.

Generate Competition Submission URL

Tool to generate a pre-signed URL for uploading competition submission files.

Get Dataset Metadata

Tool to get comprehensive metadata for a Kaggle dataset including title, description, licenses, and tags.

Get Model Details

Tool to get a Kaggle model's details including metadata and description.

Get Model Instance Details

Tool to get details for a specific Kaggle model instance (variation).

Kaggle Kernel Init

Initialize a kernel-metadata.

Download kernel output

Tool to download the output of a Kaggle kernel.

Get Kernel Status

Get the execution status of a Kaggle kernel (notebook).

List competition data files

Tool to list all data files available for a Kaggle competition.

List Kaggle Competitions

Tool to list available Kaggle competitions with filters and pagination.

List Kaggle Datasets

Tool to list Kaggle datasets with filters and pagination.

List Kernel Output Files

Tool to list output files for a specific kernel run.

List Kaggle Kernels

Tool to list Kaggle kernels (notebooks and scripts) with filters and pagination.

List Model Instance Version Files

Tool to list files for a specific version of a model variation.

List Kaggle Models

Tool to list Kaggle models with optional filters for owner, sorting, search, and pagination.

Pull Kernel Code

Tool to pull (download) the source code of a Kaggle kernel to local storage.

View competition leaderboard

Tool to view competition leaderboard information showing rankings and scores of participants.

FRAMEWORKS

How to build Kaggle MCP Agent with another framework

ChatGPT

Use Kaggle MCP with ChatGPT

OpenAI Agents SDK

Use Kaggle MCP with OpenAI Agents SDK

Claude Agent SDK

Use Kaggle MCP with Claude Agent SDK

Claude Code

Use Kaggle MCP with Claude Code

Claude Cowork

Use Kaggle MCP with Claude Cowork

Codex

Use Kaggle MCP with Codex

Cursor

Use Kaggle MCP with Cursor

OpenClaw

Use Kaggle MCP with OpenClaw

Hermes

Use Kaggle MCP with Hermes

CLI

Use Kaggle MCP with CLI

Google ADK

Use Kaggle MCP with Google ADK

LangChain

Use Kaggle MCP with LangChain

Vercel AI SDK

Use Kaggle MCP with Vercel AI SDK

Mastra AI

Use Kaggle MCP with Mastra AI

LlamaIndex

Use Kaggle MCP with LlamaIndex

CrewAI

Use Kaggle MCP with CrewAI

OpenCode

Use Kaggle MCP with OpenCode

VS Code

Use Kaggle MCP with VS Code

MORE TOOLKITS

Explore Other Toolkits

Toolkit marketplace

Composio

No Auth

Composio is an integration platform that connects AI agents with hundreds of business tools. It streamlines authentication and lets you trigger actions across services—no custom code needed.

Composio search

No Auth

Composio search is a unified web search toolkit spanning travel, e-commerce, news, financial markets, images, and more. It lets you and your apps tap into up-to-date web data from a single, easy-to-integrate service.

Perplexityai

Api Key

Perplexityai delivers natural, conversational AI models for generating human-like text. Instantly get context-aware, high-quality responses for chat, search, or complex workflows.

Browser tool

No Auth

Browser tool is a virtual browser integration that lets AI agents interact with the web programmatically. It enables automated browsing, scraping, and action-taking from any AI workflow.

FAQ

Frequently asked questions

With a standalone Kaggle MCP server, the agents and LLMs can only access a fixed set of Kaggle tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Kaggle and many other apps based on the task at hand, all through a single MCP endpoint.

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 Kaggle tools.

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

Start with Kaggle.It takes 30 seconds.

Managed auth, hosted MCP servers, and every Kaggle tool your agent needs.Free to start.

Start building

Customer stories

Teams ship real agents on Composio

GET STARTED FOR FREE BOOK A DEMO

Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

Harsha Gaddipati

Co-founder, Slashy

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

Abhi Arya

Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

Nirman Dave

CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

Ryan Yu

Founder, Extra Thursday

Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

Tomisin Jenrola

Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Jerome Leclanche

Co-Founder, Ingram Technologies

How to integrate Kaggle MCP with Autogen

Connect Kaggle without auth hassles

Introduction

Also integrate Kaggle with

TL;DR

What is AutoGen?

What is the Kaggle MCP server, and what's possible with it?

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

How the Composio SDK works

Step-by-step Guide

Prerequisites

Getting API Keys for OpenAI and Composio

Install dependencies

Set up environment variables

Import dependencies and create Tool Router session

Configure MCP parameters for Autogen

Create the model client and agent

Run the interactive chat loop

Complete Code

Conclusion

Supported Tools

How to build Kaggle MCP Agent with another framework

ChatGPT

OpenAI Agents SDK

Claude Agent SDK

Claude Code

Claude Cowork

Codex

Cursor

OpenClaw

Hermes

CLI

Google ADK

LangChain

Vercel AI SDK

Mastra AI

LlamaIndex

CrewAI

OpenCode

VS Code

Explore Other Toolkits

Composio

Composio search

Perplexityai

Browser tool

Frequently asked questions

What are the differences in Tool Router MCP and Kaggle MCP?+

Can I use Tool Router MCP with Autogen?+

Can I manage the permissions and scopes for Kaggle while using Tool Router?+

How safe is my data with Composio Tool Router?+

Start with Kaggle.It takes 30 seconds.

Teams ship real agents on Composio