Kaggle▌
by 54yyyu
Integrate with Kaggle's API for seamless competition entry, dataset management, kernels, and model submissions for data
Integrates with Kaggle's API to enable competition participation, dataset management, kernel operations, and model submissions for data scientists and machine learning practitioners.
best for
- / Data scientists participating in Kaggle competitions
- / ML practitioners searching for training datasets
- / Researchers exploring existing notebooks and models
capabilities
- / Browse and search Kaggle competitions
- / Download competition data and datasets
- / Search and analyze Kaggle notebooks/kernels
- / Access pre-trained models from Kaggle
- / Authenticate with Kaggle API credentials
what it does
Connects to Kaggle's API to browse competitions, download datasets, search kernels, and access pre-trained models. Requires Kaggle API credentials for authentication.
about
Kaggle is a community-built MCP server published by 54yyyu that provides AI assistants with tools and capabilities via the Model Context Protocol. Integrate with Kaggle's API for seamless competition entry, dataset management, kernels, and model submissions for data It is categorized under ai ml, analytics data.
how to install
You can install Kaggle in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
license
MIT
Kaggle is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
readme
README content is unavailable from source data for this server.
Open GitHub repositoryFAQ
- What is the Kaggle MCP server?
- Kaggle is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for Kaggle?
- This profile displays 10 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.
Ratings
4.5★★★★★10 reviews- ★★★★★Shikha Mishra· Oct 10, 2024
Kaggle is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Piyush G· Sep 9, 2024
We evaluated Kaggle against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Chaitanya Patil· Aug 8, 2024
Useful MCP listing: Kaggle is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Sakshi Patil· Jul 7, 2024
Kaggle reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Ganesh Mohane· Jun 6, 2024
I recommend Kaggle for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Oshnikdeep· May 5, 2024
Strong directory entry: Kaggle surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Dhruvi Jain· Apr 4, 2024
Kaggle has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Rahul Santra· Mar 3, 2024
According to our notes, Kaggle benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Pratham Ware· Feb 2, 2024
We wired Kaggle into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Yash Thakker· Jan 1, 2024
Kaggle is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.