by 54yyyu
Integrate with Kaggle's API for seamless competition entry, dataset management, kernels, and model submissions for data
Connects to Kaggle's API to browse competitions, download datasets, search kernels, and access pre-trained models. Requires Kaggle API credentials for authentication.
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.
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.
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 content is unavailable from source data for this server.
Open GitHub repository →Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
We wired Kaggle into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Kaggle has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
According to our notes, Kaggle benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Kaggle is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
We evaluated Kaggle against two servers with overlapping tools; this profile had the clearer scope statement.
Strong directory entry: Kaggle surfaces stars and publisher context so we could sanity-check maintenance before adopting.
Useful MCP listing: Kaggle is the kind of server we cite when onboarding engineers to host + tool permissions.
Kaggle has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Strong directory entry: Kaggle surfaces stars and publisher context so we could sanity-check maintenance before adopting.
I recommend Kaggle for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
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Amicus MCP Server
Amicus MCP Server: state persistence for AI coding assistants—preserves shared context, summaries, next steps and active
★ 80.5K
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.
