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container-host-aiops

by AIops-tools
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Python
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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

    A3.6/5.0

    Scored across 38 tools

    Disambiguation3/5

    Most tools are clearly scoped, but several groups overlap: dangling_images, image_disk_usage, system_df, and image_and_volume_bloat all report reclaimable disk / prune candidates, while container_restart_summary overlaps with restart_loop_rca. list_stacks vs list_compose_stacks is also easy to confuse despite the detailed descriptions.

    Naming Consistency4/5

    The set follows consistent verb_noun conventions: list_*, inspect_*, system_*, container_*, and write verbs like stop_container, start_container, prune_images, and remove_container. Minor deviations like dangling_images, stack_detail, overview, restart_loop_rca, and image_and_volume_bloat keep it from being perfectly uniform.

    Tool Count2/5

    38 tools is well above the 25+ threshold for a heavy toolset. Several read-only analysis tools (system_df, image_disk_usage, image_and_volume_bloat, dangling_images) overlap enough that the surface could be consolidated without losing real capability.

    Completeness4/5

    The read/diagnostic surface is comprehensive: containers, images, volumes, networks, system state, events, logs, stats, restart RCA, pressure analysis, and prune candidates are all covered. Container lifecycle writes (start/stop/restart/remove/update) and pruning are present; gaps are mostly targeted image/volume/network removal and compose/stack create-update flows, which are workable around.

    Maintenance

    ActivityActive
    ResponsivenessNo issues

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