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colab-persist

by ehzawad
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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

    A3.7/5.0

    Scored across 9 tools

    Disambiguation4/5

    Each tool has a distinct role in the lifecycle, and descriptions clarify boundaries. The main overlaps are restore_workspace versus prepare_workspace and save_workspaces versus safe_stop, but the descriptions make the differences clear enough for an agent to select correctly.

    Naming Consistency4/5

    Most tools follow a verb_noun pattern such as restore_workspace, save_workspaces, start_runtime, and list_checkpoints. Minor deviations like runtime_status and safe_stop break the pattern slightly but remain readable and predictable.

    Tool Count5/5

    Nine tools is well-scoped for the server's purpose of managing Colab runtimes, workspace persistence, and dataset planning. Each tool covers a meaningful lifecycle step without unnecessary bloat or redundancy.

    Completeness4/5

    The surface covers the core workflow: start/stop runtime, status, save/restore workspaces, list checkpoints, prepare/run scripts, and dataset validation. Gaps include no explicit delete/cleanup tool and no in-tool mount/unmount operation, but these are referenced as external commands and do not block the main persistence workflow.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources