Kaggle MCP vs MCP Ml Lab
A side-by-side look at the Kaggle MCP and MCP Ml Lab MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Kaggle MCP for data science tools work as a local process; choose MCP Ml Lab 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 Kaggle MCP when
- You need dedicated capabilities in the Data Science Tools domain.
- You prefer a local stdio subprocess transport.
- You have the keys it needs:
KAGGLE_USERNAME, KAGGLE_API_KEY. - You want tools like
competitions_list, competition_list_files, competition_download_file.
Choose MCP Ml Lab when
- You need dedicated capabilities in the Data Science Tools domain.
- You prefer a local stdio subprocess transport.
- You want tools like
inspect_data, define_task, run_experiment.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Data Science Tools | Data Science Tools |
| Quality signal | 48/100 · Fair | 47/100 · Fair |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | Not declared | Not declared |
| Pricing | Not declared | Not declared |
| Required env vars | KAGGLE_USERNAMEKAGGLE_API_KEY | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 0 | 0 |
| Directory activity | 1 views 0 installs 0 upvotes | 0 views 0 installs 0 upvotes |
| Tools | 38 | 5 |
| Full listing | View Kaggle MCP | View MCP Ml Lab |
Tools & capabilities
Kaggle MCP tools 38
competitions_listList and search available competitionscompetition_list_filesList all files in a competitioncompetition_download_fileDownload a specific competition filecompetition_download_filesDownload all competition filescompetition_submitSubmit predictions to a competitioncompetition_submissionsView your submission historycompetition_leaderboard_viewView the competition leaderboardcompetition_leaderboard_downloadDownload leaderboard datadatasets_listSearch and filter datasetsdataset_metadataGet dataset metadatadataset_list_filesList files in a datasetdataset_statusCheck dataset processing status- +26 more on the listing page
MCP Ml Lab tools 5
inspect_dataProfile a CSV — shape, dtypes, nulls, summary stats, class balancedefine_taskRegister an ML task (CSV + target + classification/regression)run_experimentTrain one or more models, optionally tuning with Optunaget_resultsMarkdown report with metrics, hyperparameters, feature importancecompare_runsSide-by-side comparison of multiple experiments
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.
Kaggle MCP
{
"mcpServers": {
"kaggle-mcp": {
"command": "uvx",
"args": [
"mcp-server-kaggle"
],
"env": {
"KAGGLE_USERNAME": "YOUR_KAGGLE_USERNAME_HERE",
"KAGGLE_API_KEY": "YOUR_KAGGLE_API_KEY_HERE"
}
}
}
}
MCP Ml Lab
{
"mcpServers": {
"mcp-ml-lab": {
"command": "uvx",
"args": [
"mcp-ml-lab"
]
}
}
}
Frequently asked questions
Kaggle MCP is categorized under Data Science Tools and uses a local stdio subprocess. MCP Ml Lab belongs to Data Science Tools and uses a local stdio subprocess. Pick Kaggle MCP for data science tools work and MCP Ml Lab 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.
Kaggle MCP has no declared pricing — check the project's own site. MCP Ml Lab 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 kaggle-mcp and mcp-ml-lab as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Kaggle MCP has a quality signal of 48/100 (Fair) with 1 views, 0 installs and 0 GitHub stars. MCP Ml Lab has 47/100 (Fair) with 0 views, 0 installs and 0 GitHub stars.