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.

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)

1. Go to https://www.kaggle.com/settings → API → Create New API Token
2. 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

1. competitions_list — search, category, sort_by (latestDeadline/numberOfTeams/recentlyCreated), page
2. competition_files — competition (URL suffix, e.g. titanic)
3. competition_download — competition, file_name (optional, empty = all files) → download URL
4. competition_submit — competition, blob_file_tokens, message
5. competition_submissions — competition
6. competition_leaderboard — competition → top 20 teams and scores
7. competition_get — competition → full details (deadline, reward, evaluation metric, etc.)
8. competition_data_summary — competition → data files summary dict
9. competition_get_submission — competition, submission_id (integer)
10. 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

1. datasets_list — search, sort_by (hottest/votes/updated/active), file_type, page
2. dataset_files — owner, dataset_slug
3. dataset_download — owner, dataset_slug, file_name (optional) → download URL
4. dataset_metadata — owner, dataset_slug → metadata dict
5. dataset_create — owner, slug, title, file_tokens (from file_upload), license_name, is_private
6. file_upload — file_name, content → file token for use in dataset_create
7. dataset_get — owner, dataset_slug → full dataset details
8. dataset_create_version — owner, dataset_slug, version_notes, file_tokens
9. dataset_update_metadata — owner, dataset_slug, title, description
10. dataset_delete — owner, dataset_slug
11. 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

1. kernels_list — search, competition, dataset, sort_by (hotness/commentCount/dateCreated/dateRun/relevance/voteCount), page
2. kernel_pull — user_name, kernel_slug → metadata + source code
3. kernel_push — title, text, language (python/r), kernel_type (notebook/script), is_private
4. kernel_output — user_name, kernel_slug → download URL
5. kernel_session_create — user_name, kernel_slug → session details
6. kernel_session_status — user_name, kernel_slug → status + failure message if any
7. kernel_session_output — user_name, kernel_slug → list of output files with URLs
8. kernel_session_cancel — user_name, kernel_slug
9. 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

1. models_list — search, owner, sort_by (hotness/downloadCount/createTime/updateTime), page_size
2. model_get — owner, model_slug
3. model_create — owner, slug, title, subtitle, is_private, description
4. model_update — owner, model_slug, title, subtitle, description
5. model_delete — owner, model_slug
6. model_instances_list — owner, model_slug
7. model_instance_get — owner, model_slug, framework, instance_slug
8. model_instance_create — owner, model_slug, framework, instance_slug, license_name, is_private
9. model_instance_versions — owner, model_slug, framework, instance_slug
10. 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

1. 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

1. discussions_search — query, sort_by (hotness/votes/comments/created/updated), source_type, page_size
2. discussions_list — competition, dataset, page_size, since_hours (filter to last N hours), new_only (filter by createTime vs updateTime)
3. discussion_detail — discussion_id (integer), competition (recommended for accuracy)
4. discussion_comments — discussion_id, page_size
5. discussion_comments_search — query, page_size
6. 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
7. discussions_solutions — competition (optional slug), sort_by, page_size
8. discussions_writeups — write_up_type (knowledge/competition_solution/hackathon/personal_project/forum_topic/blog), query, page_size
9. discussions_trending — source_type (optional), page_size
10. 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.