Safe Kaggle MCP

kaggle-mcp

[CI] [PyPI] [smithery] [Python] [License: MIT]

A safety-first Model Context Protocol server connecting Claude (Desktop / Code) to Kaggle — competitions, datasets, kernels (notebooks), and models.

safe-kaggle-mcp is the only Kaggle MCP built safety-first. Every irreversible action — submit, delete, public-publish — is gated behind a two-call preview→commit confirm token, a per-competition submission budget surfaced in every response, and off-by-default destructive flags, while all Kaggle-returned text is fenced in <untrusted-content> and your credentials never enter the model context. Other servers chase tool count and the official remote server wins on zero-install convenience — but none of them, official included, gate a single destructive call, cap submissions, or harden against prompt injection. It trades raw breadth for the one thing an autonomous agent with your Kaggle account actually needs: it cannot quietly burn your daily submissions, leak your key, publish your private work, or be hijacked by a poisoned discussion thread.

claude mcp add --transport stdio kaggle -- uvx --from safe-kaggle-mcp kaggle-mcp

How it compares

The two honest places we don't lead: the official remote server is zero-install, and the broadest community server (Galaxy-Dawn, ~51 tools) has more raw endpoints (e.g. benchmarks). Where they expose ~10 forum tools unfenced, we expose read-only discussion search + read with every body fenced as <untrusted-content> — and no posting tool, because the API has none.

What you can do with it

Drive your whole Kaggle workflow from a Claude chat, in plain language:

Higher-order workflows the agent chains itself: an autonomous competition loop (download → train → submit → read score → iterate, within the daily budget), Kaggle as a remote GPU/TPU backend, and cross-run memory via private dataset versions. Every irreversible step is gated, so the agent can run autonomously without risking your account.

What it won't do (honest limits)

Some competitors advertise capabilities the public Kaggle API can't actually deliver. We refuse to ship dishonest stubs:

• No posting to forums/discussions. The API can read topics but has no create/reply/vote endpoint — so we don't pretend to.
• No private-leaderboard or final-rank prediction. Private scores are withheld until the deadline; our tools label scores as public-only.
• No historical leaderboard from Kaggle. There's no such endpoint — kaggle_leaderboard_track diffs against snapshots you captured locally, and says so.
• No hidden test labels / private splits. We only ever serve files the competition exposes.

Install

# fastest: install + register with Claude Codeclaude mcp add --transport stdio kaggle -- uvx --from safe-kaggle-mcp kaggle-mcp
# or straight from this repo (no PyPI):claude mcp add --transport stdio kaggle -- uvx --from git+https://github.com/parkseokjune/kaggle-mcp kaggle-mcp
# or for developmentuv sync --extra dev && uv run kaggle-mcp

The PyPI distribution is safe-kaggle-mcp (the name kaggle-mcp was taken); the command it installs is still kaggle-mcp, so launch it with uvx --from safe-kaggle-mcp kaggle-mcp.

Get a token at https://www.kaggle.com/settings → API → Create New Token. Both schemes work (resolved in order):

# 1) legacy username/key (env or ~/.kaggle/kaggle.json)export KAGGLE_USERNAME=your_userexport KAGGLE_KEY=your_40_char_key
# 2) current API token (KGAT_...): the client reads it automaticallymkdir -p ~/.kaggle && echo "KGAT_..." > ~/.kaggle/access_token && chmod 600 ~/.kaggle/access_token

Claude Desktop — edit claude_desktop_config.json, then fully restart:

{ "mcpServers": { "kaggle": {  "command": "uvx", "args": ["--from", "safe-kaggle-mcp", "kaggle-mcp"],  "env": { "KAGGLE_USERNAME": "your_user", "KAGGLE_KEY": "your_key" } } } }

Safety switches (default OFF → read-only / private posture)

Even when enabled, each destructive call still needs a one-time confirm_token from its *_preview_* tool, and is recorded in kaggle_audit_log.

Tools

41 tools across account, competitions, datasets, discussions, kernels, models — plus kaggle:// resources (metadata, leaderboard, rules) and /kaggle-eda, /kaggle-submit-checklist, /kaggle-landscape, /kaggle-solution-research prompts. Run kaggle_status to see your auth + submission budget.

Verified live against the Kaggle API (kaggle 2.2.2) with both auth schemes; 53 offline + 6 live read-only tests pass.

Develop

uv run pytest                              # offline unit + integration testsKAGGLE_LIVE=1 uv run pytest tests/test_live_readonly.py   # live read-only smoke (needs creds)uv run mcp dev src/kaggle_mcp/server.py    # MCP Inspector

License

MIT