mcp-kaggle-tool
Integrates with Kaggle API to create, run, and manage Kaggle notebooks; search datasets and competitions; download outputs; and monitor execution status.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@mcp-kaggle-tool create a notebook titled 'My ARC Experiment' with GPU enabled"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
mcp-kaggle-tool
MCP server for Kaggle API integration - create, run, and manage Kaggle notebooks programmatically.
π Features
β Authentication check for Kaggle API
π Create and manage Kaggle notebooks
π Run notebooks with GPU support
π Search datasets and competitions
πΎ Download notebook outputs
π Monitor execution status
Related MCP server: kaggle-mcp-server
π Prerequisites
Kaggle Account: You need a Kaggle account
Kaggle API Token:
Click "Create New API Token"
Save the downloaded
kaggle.jsonto~/.kaggle/
Kaggle CLI: Install the Kaggle CLI:
pip install kaggle
π οΈ Installation
From npm (when published)
npm install -g mcp-kaggle-toolFrom source
git clone https://github.com/yourusername/mcp-kaggle-tool.git
cd mcp-kaggle-tool
npm install
npm run buildπ§ Configuration
For Claude Desktop
Add to your Claude Desktop configuration (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):
{
"mcpServers": {
"kaggle": {
"command": "npx",
"args": ["mcp-kaggle-tool"]
}
}
}Or if running from source:
{
"mcpServers": {
"kaggle": {
"command": "node",
"args": ["/path/to/mcp-kaggle-tool/dist/index.js"]
}
}
}π Available Tools
Authentication
kaggle_auth_check- Verify Kaggle API credentials are configured
Notebooks
kaggle_list_notebooks- List your Kaggle notebookskaggle_create_notebook- Create a new notebook with codekaggle_run_notebook- Execute a notebookkaggle_get_notebook_status- Check execution statuskaggle_download_notebook_output- Download notebook outputs
Data & Competitions
kaggle_search_datasets- Search for datasetskaggle_list_competitions- List active competitions
π‘ Usage Examples
Check Authentication
Use kaggle_auth_check to verify your credentials are set upCreate and Run a Notebook
1. Create a notebook with kaggle_create_notebook:
- title: "My ARC Experiment"
- code: "print('Hello from Kaggle!')"
- enableGpu: true
2. Monitor with kaggle_get_notebook_status
3. Download results with kaggle_download_notebook_outputSearch ARC Dataset
Use kaggle_search_datasets with search: "abstraction reasoning corpus"π§ Development
# Install dependencies
npm install
# Build TypeScript
npm run build
# Run in development
npm run dev
# Run tests
npm test
# Lint code
npm run lintπ License
MIT License - see LICENSE file for details.
π€ Contributing
Contributions welcome! Please open an issue or submit a PR.
π Known Issues
Kaggle API sometimes returns HTML instead of JSON for certain commands
Notebook execution status may take time to update
GPU availability depends on Kaggle quota
π Resources
Available Tools
8 toolskaggle_auth_checkA
Check if Kaggle API credentials are configured
Inputs: no named parameters.
kaggle_create_notebookB
Create a new Kaggle notebook
Inputs: title (required), code (required), language, isPrivate, +3 more.
kaggle_download_notebook_outputC
Download the output of a completed notebook
Inputs: kernelSlug (required), outputPath.
kaggle_get_notebook_statusB
Get the status of a notebook execution
Inputs: kernelSlug (required).
kaggle_list_competitionsC
List Kaggle competitions
Inputs: group, category, sortBy.
kaggle_list_notebooksB
List your Kaggle notebooks (kernels)
Inputs: page, pageSize.
kaggle_run_notebookC
Run/execute a Kaggle notebook
Inputs: kernelSlug (required).
kaggle_search_datasetsC
Search for Kaggle datasets
Inputs: search (required), page.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelogβ¦
TDQS
Scored across 8 tools
Each tool has a clearly distinct purpose: auth check, notebook CRUD, status, output download, listing competitions/notebooks, and dataset search. No overlapping functionality.
All tools follow a consistent 'kaggle_verb_noun' pattern with underscores, making it easy to predict tool names.
With 8 tools, the server covers the main Kaggle operations (authentication, notebook lifecycle, listings, dataset search) without being too sparse or bloated.
Covers notebook lifecycle well (create, run, status, download) and includes listings for competitions and notebooks, but lacks dataset download, competition submission, and credential management beyond check.
Maintenance
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