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MOT1209

Google Colab MCP Server

by MOT1209
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Python
Hybrid

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    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

    B3.2/5.0

    Scored across 40 tools

    Disambiguation4/5

    Most tools follow a clear resource-action split (notebook, file, package, runtime, job), so an agent can usually pick the right one. However, cancel_job, stop_training, and stop_execution overlap in purpose, and save_model/export_model are difficult to distinguish without reading deeply.

    Naming Consistency5/5

    All tools use the colab_ prefix with verb_noun snake_case, making the API highly predictable. Even large groups like colab_get_gpu/cpu/memory/disk follow a uniform pattern.

    Tool Count2/5

    Forty tools is a large surface for a single MCP server, falling into the 'too heavy' range. Even though each tool is individually meaningful, the set bundles notebook, file, package, runtime, and job-management responsibilities that could be separate servers.

    Completeness4/5

    The toolset covers notebook CRUD/cell editing/execution, file management, package management, runtime introspection, and training-job lifecycle. Minor gaps exist—no dedicated notebook delete/list or cell retrieval-by-index—but file tools and execute options provide workarounds.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources