MCP Servers

kaggle-mcp-server

A full-featured MCP server with 96 tools for the Kaggle API, enabling users to manage competitions, datasets, notebooks, models, discussions, and workflows via natural language.

README

Kaggle MCP Server

A full-featured Model Context Protocol (MCP) server for the Kaggle API — 96 tools across competitions, datasets, kernels, models, benchmarks, discussions, and workflow utilities.

Installation

pip install kaggle-mcp-server

Prerequisites

Kaggle API credentials — place your kaggle.json at ~/.kaggle/kaggle.json:

{"username":"YOUR_USERNAME","key":"YOUR_API_KEY"}

Get your API key from kaggle.com/settings → "Create New Token".

Python 3.12+

Usage

With Cursor IDE

Add to .cursor/mcp.json:

{
  "mcpServers": {
    "kaggle": {
      "command": "kaggle-mcp-server"
    }
  }
}

With Claude Desktop

Add to your Claude Desktop config:

{
  "mcpServers": {
    "kaggle": {
      "command": "kaggle-mcp-server"
    }
  }
}

Standalone

kaggle-mcp-server

Tools (96 total)

Competitions (16 tools)

Tool	Description
`competitions_list`	Search and list competitions
`competition_get`	Get detailed competition info
`competition_files`	List competition data files
`competition_tree_files`	Hierarchical tree view of competition data
`competition_download`	Download competition data (returns URL)
`competition_download_single_file`	Download a single competition file locally
`competition_submit`	Submit predictions via blob token
`submit_local_file`	Submit a local prediction file
`submit_code_competition`	Submit to code competitions
`competition_submissions`	View submission history
`competition_get_submission`	Get single submission details
`submission_score`	Get/poll submission score
`competition_leaderboard`	View top 20 leaderboard
`competition_leaderboard_download`	Download full leaderboard
`leaderboard_position`	Find a team/user's rank
`competition_data_summary`	Get data files summary

Competition Workflow (5 tools)

Tool	Description
`setup_comp`	Download and extract competition data locally
`competition_full_setup`	One-shot setup: info + download + preview
`upcoming_deadlines`	Show competitions with closest deadlines
`my_competitions`	List competitions you've entered
`competition_top_kernels`	Top public notebooks for a competition

Datasets (20 tools)

Tool	Description
`datasets_list`	Search and list datasets
`dataset_get`	Get full dataset info
`dataset_files`	List files in a dataset
`dataset_tree_files`	Hierarchical tree view of dataset files
`dataset_files_summary`	Get file count and total size
`dataset_download`	Download dataset (returns URL)
`dataset_download_file`	Download a single dataset file
`download_dataset_local`	Download and extract dataset locally
`dataset_metadata`	Get dataset metadata
`dataset_update_metadata`	Update title/description/license
`dataset_create`	Create dataset via blob tokens
`create_dataset_from_files`	Create dataset from local directory
`dataset_create_version`	Create new version via tokens
`push_dataset_version`	Push new version from local directory
`dataset_delete`	Delete a dataset
`dataset_status`	Check dataset processing status
`file_upload`	Upload file and get blob token
`my_datasets`	List your datasets
`datasets_by_user`	List datasets by a specific user
`check_dataset_exists`	Check if a dataset exists

Kernels / Notebooks (15 tools)

Tool	Description
`kernels_list`	Search and list notebooks
`kernel_pull`	Get notebook source code
`kernel_push`	Push/save a notebook
`push_notebook_file`	Push local .ipynb to Kaggle
`kernel_output`	Download kernel output (URL)
`kernel_download_output_zip`	Download kernel output locally
`kernel_status`	Check kernel execution status
`kernel_files`	List kernel files
`kernel_delete`	Delete a kernel
`kernel_initialize`	Initialize kernel template locally
`kernel_session_create`	Create interactive session
`kernel_session_status`	Get session status
`kernel_session_output`	List session output files
`kernel_session_cancel`	Cancel running session
`generate_notebook_metadata`	Generate kernel-metadata.json

Models (16 tools)

Tool	Description
`models_list`	Search and list models
`model_get`	Get model details
`model_create`	Create a new model
`model_update`	Update model info
`model_delete`	Delete a model
`model_metrics`	Get model performance metrics
`model_instances_list`	List model instances
`model_instance_get`	Get instance details
`model_instance_create`	Create a new instance
`model_instance_delete`	Delete an instance
`model_instance_files`	List instance files
`model_instance_versions`	List instance versions
`model_instance_version_create`	Create new version
`model_instance_version_download`	Download version files
`model_instance_version_files`	List version files
`model_instance_version_delete`	Delete a version

Discussions (10 tools)

Tool	Description
`discussions_search`	Search discussions
`discussions_list`	List discussions for competition/dataset
`discussion_detail`	Get discussion content
`discussion_comments`	Get discussion comments
`discussion_comments_search`	Search across all comments
`discussions_by_source`	Browse by source type
`discussions_solutions`	Browse competition solutions
`discussions_writeups`	Browse write-ups by type
`discussions_trending`	Browse trending discussions
`discussions_my`	List your discussions

Benchmarks (1 tool)

Tool	Description
`benchmark_leaderboard`	Get benchmark leaderboard

Data Preview & Analysis (4 tools)

Tool	Description
`preview_csv`	Preview first N rows of a CSV
`preview_data_file`	Preview any text data file
`csv_column_analysis`	Analyze column types and stats
`compare_csvs`	Diff two CSV files

Workflow Utilities (9 tools)

Tool	Description
`generate_starter_notebook`	Auto-generate competition starter notebook
`search_everything`	Unified search across competitions, datasets, notebooks
`list_local_files`	List local files with sizes
`track_operation`	Monitor long-running Kaggle operations
`download_pip_library`	Download pip wheels for offline use
`download_pip_requirements`	Download requirements.txt wheels
`create_library_dataset`	Upload pip library as Kaggle dataset
`create_requirements_dataset`	Upload requirements as Kaggle dataset
`get_local_library_version`	Check local wheel version

License

MIT

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