kaggle-context
Related Servers
Alternatives to kaggle-context
No user-submitted related servers found.
Related Servers
- AlicenseAqualityCmaintenanceA safety-first MCP server that connects Claude to Kaggle, enabling competition management, dataset analysis, submission tracking, and kernel execution with built-in safeguards against destructive actions and prompt injection.4124 PyPIMIT
- AlicenseNot gradedqualityCmaintenanceEnables AI agents and CLI users to drive Kaggle notebooks end-to-end—pull, edit, save new versions, run, monitor, and fetch logs—using a local stdio MCP server with a Kaggle API token.MIT
- AlicenseBqualityDmaintenanceA full-featured MCP server for the Kaggle API — competitions, datasets, kernels, models, benchmarks, and discussions.5135 PyPI4MIT
- AlicenseNot gradedqualityDmaintenanceA full-featured MCP server with 96 tools for the Kaggle API, enabling users to manage competitions, datasets, notebooks, models, discussions, and workflows via natural language.MIT
- AlicenseNot gradedqualityDmaintenanceA Model Context Protocol (MCP) server that provides seamless integration with the Kaggle API, enabling interaction with competitions, datasets, kernels, and models through MCP-compatible clients.32 PyPIMIT
- AlicenseNot gradedqualityFmaintenanceA local MCP server that integrates with Claude Desktop, enabling RAG capabilities to provide Claude with up-to-date private information from custom LlamaCloud indices.225MIT
TDQS
Scored across 10 tools
Most tools target distinct resources or actions, but fetch_competition says it returns the brief while get_brief also returns key facts, creating minor overlap in when to use each. Discussion and notebook tools are clearly separated.
All tools use snake_case with predictable verb_noun patterns (list_*, fetch_*, get_*, search_*), and the one compound list_top_notebooks remains readable and consistent.
Ten tools is well-scoped for a Kaggle context server, covering competitions, discussions, notebooks, and updates without excessive surface area.
Core lifecycle for fetching and reading competition context is covered, including sections, discussions, notebooks, and live updates. Minor gaps exist around general competition search or non-top notebook discovery, but agents can work around them via fetch/reference tools.