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AI Guardian

by AIops-tools
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Python
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    TDQS

    A3.6/5.0

    Scored across 21 tools

    Disambiguation4/5

    Most tools target clearly distinct actions/resources: scanning (scan_prompt), generation (guarded_generate), chat (observe_chat), policy (allowlist/denylist), and state reads (server_status, policy_view). The one soft cluster is model introspection—list_models, running_models, model_details, and vram_usage—but their descriptions clearly separate installed, loaded, single-model, and aggregate-VRAM views.

    Naming Consistency3/5

    There is a loose but not reliable pattern: action tools are verb_noun (pull_model, remove_model, unload_model, scan_prompt, set_model_allowlist, pin_model_digest, undo_apply) while many state tools are noun phrases (server_status, policy_view, vram_usage, usage_events, anomaly_report, model_provenance). The mix is readable and grep-able, but not predictable enough to be a clean convention.

    Tool Count3/5

    At 21 tools this is on the heavy side for the rubric's 3-15 sweet spot, spanning model introspection, model lifecycle, policy/provenance, scanning/governance, usage analytics, undo, and proxy guidance. Most tools appear justified by a genuinely broad domain, but the surface is large enough that count is borderline rather than well-scoped.

    Completeness4/5

    Coverage is strong: model lifecycle (list/pull/remove/unload/details), policy management (view/allow/deny/pin/provenance), governance (scan, guarded_generate, observe_chat, usage_events, anomaly_report), and an undo mechanism (undo_list/undo_apply). Minor gaps remain—allowlist/denylist can only be replaced wholesale (no per-model toggle), and there is no persistent block-threshold configuration tool—but core workflows are covered without dead ends.

    Maintenance

    ActivityActive
    ResponsivenessNo issues

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