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MOT1209

Google Colab MCP Server

by MOT1209
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Python
Hybrid

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    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

    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