How to integrate Kaggle MCP with Pydantic AI

This guide walks you through connecting Kaggle to Pydantic AI 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 Pydantic AI 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.

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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

Introduction

This guide walks you through connecting Kaggle to Pydantic AI 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 Pydantic AI 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

ChatGPTOpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenCodeCursorVS CodeOpenClawHermesCLIGoogle 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 Kaggle
  • 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 Kaggle 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 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:

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

ChatGPT

Use Kaggle MCP with ChatGPT

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Kaggle MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Kaggle MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Kaggle MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Kaggle MCP with Claude Cowork

Codex logo

Codex

Use Kaggle MCP with Codex

Cursor logo

Cursor

Use Kaggle MCP with Cursor

OpenClaw logo

OpenClaw

Use Kaggle MCP with OpenClaw

Hermes logo

Hermes

Use Kaggle MCP with Hermes

CLI logo

CLI

Use Kaggle MCP with CLI

Google ADK logo

Google ADK

Use Kaggle MCP with Google ADK

LangChain logo

LangChain

Use Kaggle MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Kaggle MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Kaggle MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Kaggle MCP with LlamaIndex

CrewAI logo

CrewAI

Use Kaggle MCP with CrewAI

OpenCode logo

OpenCode

Use Kaggle MCP with OpenCode

VS Code logo

VS Code

Use Kaggle MCP with VS Code

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. 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 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.

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