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Kaggle MCP

A full-featured MCP server for the Kaggle API — competitions, datasets, kernels, models, benchmarks, and discussions.

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A full-featured MCP server for the Kaggle API — competitions, datasets, kernels, models, benchmarks, and discussions.

English | 中文

Why kaggle-mcp?

Kaggle provides an official remote MCP server (https://www.kaggle.com/mcp) covering competitions, datasets, notebooks, models, and benchmarks — a solid foundation for most Kaggle workflows.

kaggle-mcp extends that foundation with what the official server is missing: 10 discussion tools. You can search discussions, browse by source type, filter competition discussions by recency, read solution write-ups, explore trending topics, and more — none of which are available in the official MCP.

It also runs locally over stdio, so there's no remote MCP dependency and no npx mcp-remote required.

Use kaggle-mcp if you need discussion tools or prefer a local stdio setup without remote dependencies. Use the official MCP if you prefer OAuth 2.0 auth or want zero local installation.

kaggle-mcp architecture

Quick Navigation

Section	Description
Prerequisites	Kaggle API token setup
Installation	uvx / pip / source
Configuration	Claude Desktop, Claude Code, VS Code, Cursor
Tools (51)	Competitions, Datasets, Kernels, Models, Benchmarks, Discussions
Debugging	MCP Inspector
Development	Local development setup

Prerequisites

A Kaggle API token is required. You can authenticate using either method:

Option A: API Token (recommended)

Go to https://www.kaggle.com/settings → API → Create New API Token
Set the environment variable:

export KAGGLE_API_TOKEN="KGAT_xxxxxxxxxxxx"

Option B: kaggle.json

Download the token file from Kaggle settings, it will be saved to ~/.kaggle/kaggle.json:

{"username": "your_username", "key": "your_api_key"}

Installation

Note: MCP servers are launched automatically by MCP clients (Claude Code, VS Code, etc.) — you don't need to run them manually in the terminal. The commands below are what the client uses under the hood.

Using uvx (recommended)

No installation needed. uvx will automatically download and run the server:

# Used by MCP clients internally; no need to run this yourself
uvx kaggle-mcp-server

Using pip

pip install kaggle-mcp-server

From source

git clone https://github.com/Galaxy-Dawn/kaggle-mcp.git
cd kaggle-mcp
uv sync

Configuration

Claude Desktop

Add to your claude_desktop_config.json:

Using uvx (recommended)

{
  "mcpServers": {
    "kaggle": {
      "command": "uvx",
      "args": ["kaggle-mcp-server"],
      "env": {
        "KAGGLE_API_TOKEN": "KGAT_xxxxxxxxxxxx"
      }
    }
  }
}

Using pip

{
  "mcpServers": {
    "kaggle": {
      "command": "python",
      "args": ["-m", "kaggle_mcp.server"],
      "env": {
        "KAGGLE_API_TOKEN": "KGAT_xxxxxxxxxxxx"
      }
    }
  }
}

Claude Code

claude mcp add kaggle -- uvx kaggle-mcp-server

Or add to your project's .mcp.json (not settings.json):

{
  "mcpServers": {
    "kaggle": {
      "command": "uvx",
      "args": ["kaggle-mcp-server"],
      "env": {
        "KAGGLE_API_TOKEN": "KGAT_xxxxxxxxxxxx"
      }
    }
  }
}

VS Code

Add to .vscode/mcp.json (note: the key is "servers", not "mcpServers"):

{
  "servers": {
    "kaggle": {
      "command": "uvx",
      "args": ["kaggle-mcp-server"],
      "env": {
        "KAGGLE_API_TOKEN": "KGAT_xxxxxxxxxxxx"
      }
    }
  }
}

Cursor

Add to .cursor/mcp.json:

{
  "mcpServers": {
    "kaggle": {
      "command": "uvx",
      "args": ["kaggle-mcp-server"],
      "env": {
        "KAGGLE_API_TOKEN": "KGAT_xxxxxxxxxxxx"
      }
    }
  }
}

Tip: If you already have KAGGLE_API_TOKEN in your shell environment (e.g. in .bashrc or .zshrc), you can omit the "env" block.

Tools (51)

Competitions (10)

Tool	Description
`competitions_list`	Search and list Kaggle competitions
`competition_files`	List data files for a competition
`competition_download`	Download competition data files
`competition_submit`	Submit predictions to a competition
`competition_submissions`	View submission history
`competition_leaderboard`	View leaderboard (top 20)
`competition_get`	Get detailed competition info
`competition_data_summary`	Get data files summary
`competition_get_submission`	Get details for a single submission
`competition_leaderboard_download`	Download full leaderboard as CSV

Parameter details

competitions_list — search, category, sort_by (latestDeadline/numberOfTeams/recentlyCreated), page
competition_files — competition (URL suffix, e.g. titanic)
competition_download — competition, file_name (optional, empty = all files) → download URL
competition_submit — competition, blob_file_tokens, message
competition_submissions — competition
competition_leaderboard — competition → top 20 teams and scores
competition_get — competition → full details (deadline, reward, evaluation metric, etc.)
competition_data_summary — competition → data files summary dict
competition_get_submission — competition, submission_id (integer)
competition_leaderboard_download — competition → download URL for full leaderboard CSV

Datasets (11)

Tool	Description
`datasets_list`	Search and list Kaggle datasets
`dataset_files`	List files in a dataset
`dataset_download`	Download dataset files
`dataset_metadata`	Get dataset metadata
`dataset_create`	Create a new dataset
`file_upload`	Upload a file to Kaggle
`dataset_get`	Get full dataset information
`dataset_create_version`	Create a new dataset version
`dataset_update_metadata`	Update dataset title/description
`dataset_delete`	Delete a dataset
`dataset_download_file`	Download a single file from a dataset

Parameter details

datasets_list — search, sort_by (hottest/votes/updated/active), file_type, page
dataset_files — owner, dataset_slug
dataset_download — owner, dataset_slug, file_name (optional) → download URL
dataset_metadata — owner, dataset_slug → metadata dict
dataset_create — owner, slug, title, file_tokens (from file_upload), license_name, is_private
file_upload — file_name, content → file token for use in dataset_create
dataset_get — owner, dataset_slug → full dataset details
dataset_create_version — owner, dataset_slug, version_notes, file_tokens
dataset_update_metadata — owner, dataset_slug, title, description
dataset_delete — owner, dataset_slug
dataset_download_file — owner, dataset_slug, file_name → download URL

Kernels (9)

Tool	Description
`kernels_list`	Search and list notebooks/kernels
`kernel_pull`	Get a notebook's source code
`kernel_push`	Push/save a notebook to Kaggle
`kernel_output`	Get kernel output download URL
`kernel_session_create`	Create an interactive kernel session
`kernel_session_status`	Get kernel session execution status
`kernel_session_output`	List output files from a kernel session
`kernel_session_cancel`	Cancel a running kernel session
`competition_top_kernels`	List top public kernels for a competition sorted by score

Parameter details

kernels_list — search, competition, dataset, sort_by (hotness/commentCount/dateCreated/dateRun/relevance/voteCount), page
kernel_pull — user_name, kernel_slug → metadata + source code
kernel_push — title, text, language (python/r), kernel_type (notebook/script), is_private
kernel_output — user_name, kernel_slug → download URL
kernel_session_create — user_name, kernel_slug → session details
kernel_session_status — user_name, kernel_slug → status + failure message if any
kernel_session_output — user_name, kernel_slug → list of output files with URLs
kernel_session_cancel — user_name, kernel_slug
competition_top_kernels — competition, sort_by (scoreDescending/scoreAscending/voteCount/hotness/dateCreated/dateRun/commentCount), page_size — Note: Kaggle API does not expose score values for active competitions; scores are extracted from notebook titles where authors include them (e.g. [0.371], LB:0.95)

Models (10)

Tool	Description
`models_list`	Search and list Kaggle models
`model_get`	Get detailed model information
`model_create`	Create a new model
`model_update`	Update model metadata
`model_delete`	Delete a model
`model_instances_list`	List all instances of a model
`model_instance_get`	Get a specific model instance
`model_instance_create`	Create a new model instance
`model_instance_versions`	List versions of a model instance
`model_instance_version_create`	Create a new model instance version

Parameter details

models_list — search, owner, sort_by (hotness/downloadCount/createTime/updateTime), page_size
model_get — owner, model_slug
model_create — owner, slug, title, subtitle, is_private, description
model_update — owner, model_slug, title, subtitle, description
model_delete — owner, model_slug
model_instances_list — owner, model_slug
model_instance_get — owner, model_slug, framework, instance_slug
model_instance_create — owner, model_slug, framework, instance_slug, license_name, is_private
model_instance_versions — owner, model_slug, framework, instance_slug
model_instance_version_create — owner, model_slug, framework, instance_slug, version_notes, file_tokens

Benchmarks (1)

Tool	Description
`benchmark_leaderboard`	Get benchmark leaderboard

Parameter details

benchmark_leaderboard — owner_slug, benchmark_slug, version_number (optional, default 0)

Discussions (10)

Tool	Description
`discussions_search`	Search Kaggle discussions
`discussions_list`	List discussions for a competition/dataset
`discussion_detail`	Get discussion content by ID
`discussion_comments`	Get comments for a discussion
`discussion_comments_search`	Search comments across all discussions
`discussions_by_source`	Browse discussions by source type
`discussions_solutions`	Browse competition solution write-ups
`discussions_writeups`	Browse Kaggle write-ups by type
`discussions_trending`	Browse trending discussions
`discussions_my`	List the current user's discussions

Parameter details

discussions_search — query, sort_by (hotness/votes/comments/created/updated), source_type, page_size
discussions_list — competition, dataset, page_size, since_hours (filter to last N hours), new_only (filter by createTime vs updateTime)
discussion_detail — discussion_id (integer), competition (recommended for accuracy)
discussion_comments — discussion_id, page_size
discussion_comments_search — query, page_size
discussions_by_source — source_type (competition/dataset/kernel/site_forum/competition_solution/model/write_up/learn_track/benchmark/benchmark_task), query, sort_by, page_size
discussions_solutions — competition (optional slug), sort_by, page_size
discussions_writeups — write_up_type (knowledge/competition_solution/hackathon/personal_project/forum_topic/blog), query, page_size
discussions_trending — source_type (optional), page_size
discussions_my — page_size

Debugging

You can use the MCP Inspector to debug the server:

npx @modelcontextprotocol/inspector uvx kaggle-mcp-server

The Inspector will provide a URL to access debugging tools in your browser.

Development

git clone https://github.com/Galaxy-Dawn/kaggle-mcp.git
cd kaggle-mcp
uv sync

Then configure the server in your MCP client using the local path, or test with MCP Inspector.

Contributing

Contributions are welcome! Please open an issue or submit a pull request on the GitHub repository.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Kaggle MCP

Installation

Reviews

Documentation

Why kaggle-mcp?

Quick Navigation

Prerequisites

Installation

Using uvx (recommended)

Using pip

From source

Configuration

Claude Desktop

Claude Code

VS Code

Cursor

Tools (51)

Competitions (10)

Datasets (11)

Kernels (9)

Models (10)

Benchmarks (1)

Discussions (10)

Debugging

Development

Contributing

License

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