Skip to main content
Glama
mtrakretech

Kaggle MCP Server

by mtrakretech
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Related Servers

Alternatives to Kaggle MCP Server

No user-submitted related servers found.

    Related Servers

    • Kaggle MCP Server

      Dishant27
      F
      license
      Not graded
      quality
      D
      maintenance
      Enables AI assistants to interact with Kaggle competitions, including listing competitions, downloading files, submitting predictions, and viewing submission history.
      10
      -

    • kaggle-mcp

      parkseokjune
      A
      license
      A
      quality
      C
      maintenance
      A 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.
      41
      15 PyPI
      MIT

    • Kaggle-MCP

      realbytecode
      A
      license
      Not graded
      quality
      D
      maintenance
      Connects Claude AI to the Kaggle API through the Model Context Protocol, enabling users to browse competitions, search and download datasets, analyze kernels, and access pre-trained models through natural language interactions.
      MIT
    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    C2.6/5.0

    Scored across 68 tools

    Disambiguation3/5

    The verb_noun structure separates most actions, but several pairs blur together: kernel_files/kernel_output, dataset_details/get_dataset_metadata, model_details/get_model_instance, and get_quota/get_account_info all require close reading. The many model/instance/version lifecycle variants are easy to misselect without careful attention to the resource level.

    Naming Consistency4/5

    Tool names mostly follow a consistent snake_case verb_noun convention (list_, create_, update_, delete_, download_, init_). Minor inconsistencies exist: kernel and notebook are used interchangeably for the same resource, competition_leaderboard and dataset_details lack a verb, and update_dataset means 'new version' while update_model means metadata-only update.

    Tool Count1/5

    68 tools is far beyond a reasonable MCP surface and exceeds the 50+ threshold for an extreme count. While the tools cover distinct Kaggle domains, the server would be much more usable split into separate dataset, kernel, competition, and model servers.

    Completeness5/5

    The tool surface is exhaustive across Kaggle's main workflows: dataset, kernel, and model CRUD/lifecycle operations, competition submission flows, discussion forums, and account/quota information. Core workflows have no obvious dead ends, and the high count is largely due to genuinely broad domain coverage.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources