Jupyter MCP Server vs Safe Kaggle MCP
A side-by-side look at the Jupyter MCP Server and Safe Kaggle MCP MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Jupyter MCP Server for data science tools work as a local process; choose Safe Kaggle MCP for data science tools as a local process. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Jupyter MCP Server when
- You need dedicated capabilities in the Data Science Tools domain.
- You prefer a local stdio subprocess transport.
- Its auth model fits you: No auth required.
Choose Safe Kaggle MCP when
- You need dedicated capabilities in the Data Science Tools domain.
- You prefer a local stdio subprocess transport.
- You want tools like
kaggle_whoami, kaggle_status, kaggle_audit_log.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Data Science Tools | Data Science Tools |
| Quality signal | 48/100 · Fair | 53/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | No auth required | Not declared |
| Pricing | Free | Not declared |
| Required env vars | None listed | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 1,312 | 0 |
| Directory activity | 3 views 0 installs 0 upvotes | 1 views 0 installs 0 upvotes |
| Tools | Not listed | 41 |
| Full listing | View Jupyter MCP Server | View Safe Kaggle MCP |
Tools & capabilities
Jupyter MCP Server tools
No tool names declared yet. Check the project README from the listing page.
Safe Kaggle MCP tools 41
kaggle_whoamiReturn the authenticated Kaggle username and credential source. Does not transmit or echo the API key. Use to confirm the server resolved credentials for the expected account.kaggle_statusHealth check: credential validity, current per-competition submission budgets for this session, enabled safety switches, and the work-dir root.kaggle_audit_logReturn the session's append-only ledger of mutating actions (submit / create / version / delete) with timestamps and the submission budget at the time — every field redacted. Makes the safety machinery inspectable, which no other Kaggle MCP can offer (none track tokens or budgets).kaggle_list_competitionsList/triage competitions as a compact ranked table (title, slug, prize, deadline, evaluation metric, category). Paginated. Read-only.kaggle_competition_landscapePre-digested triage report of competitions: for each, the slug, prize, evaluation metric, deadline, and **days_left** — sorted by soonest deadline so you can decide what to enter at a glance. A single decision-ready artifact rather than raw endpoint output (which is what other Kaggle MCPs return).kaggle_get_competitionFetch one competition's details and a truncated, untrusted-wrapped rules digest. `competition` is the BARE slug (e.g. 'titanic'). Read-only.kaggle_competition_leaderboardPublic leaderboard capped to top-N (default 20) to protect context.kaggle_leaderboard_trackSnapshot the public top-N and diff it against the LAST snapshot you took: per-team rank deltas, new entrants, biggest climbers, and (if your_team is given) your movement + who passed you. Kaggle has no historical-leaderboard endpoint, so this stateful local tracking is unique. PUBLIC leaderboard only; deltas are vs your previous snapshot, not an absolute time series.kaggle_download_competition_filesDownload competition data into an isolated work dir (zip auto-extracted with a zip-slip guard). Returns local paths + metadata, NOT file contents. Requires the competition's rules accepted (403 otherwise).kaggle_eda_competitionDownload a competition's data and return a pre-first-submission view: a compact pandas digest per CSV (shape/dtypes/missingness/correlations) PLUS a train-vs-test column diff that auto-infers the target — the orientation no other Kaggle MCP ships. Requires the competition rules accepted (403 else).kaggle_competition_kickoffOne-call competition kickoff: fetch the metric/deadline/rules, download the data if rules are accepted (otherwise tell you to accept first), run a train/test EDA with target auto-detection, and return a baseline plan plus your remaining submission budget. Collapses the manual setup ritual into a single decision-ready bundle — no other Kaggle MCP does this.kaggle_submission_best_scoreReduce raw submission history to the decision signal: best public score (respecting metric direction), first/best/latest trend, today's submission count, and any failure reasons — the 'is it worth iterating?' answer other servers leave as a raw list. Public scores only (private score is hidden until the deadline).- +29 more on the listing page
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Jupyter MCP Server
{
"mcpServers": {
"datalayer-jupyter-mcp-server": {
"command": "uvx",
"args": [
"jupyter-mcp-server@latest"
]
}
}
}
Safe Kaggle MCP
{
"mcpServers": {
"safe-kaggle-mcp": {
"command": "uvx",
"args": [
"--from",
"safe-kaggle-mcp",
"kaggle-mcp"
]
}
}
}
Frequently asked questions
Jupyter MCP Server is categorized under Data Science Tools and uses a local stdio subprocess. Safe Kaggle MCP belongs to Data Science Tools and uses a local stdio subprocess. Pick Jupyter MCP Server for data science tools work and Safe Kaggle MCP for data science tools.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Jupyter MCP Server is listed as free. Auth: no auth required. Safe Kaggle MCP has no declared pricing — check the project's own site. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add datalayer-jupyter-mcp-server and safe-kaggle-mcp as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Jupyter MCP Server has a quality signal of 48/100 (Fair) with 3 views, 0 installs and 1,312 GitHub stars. Safe Kaggle MCP has 53/100 (Good) with 1 views, 0 installs and 0 GitHub stars.