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by nwjnjjj

Related Servers

Alternatives to smart-token-guard

No user-submitted related servers found.

    Related Servers

    • A
      license
      Not graded
      quality
      B
      maintenance
      Turns silent defects in AI-generated video and audio — wrong speaking pace, mismatched loudness, truncation, dead air, late captions, broken layout — into hard assertion failures inside tests and CI. Enables agents to check individual files or whole output directories against platform presets, returning pass/fail exit codes and human-readable bug reports instead of raw measurements.
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables AI agents to inspect video camera clips through structured quantitative measurements such as exposure, color, sharpness, noise, and camera motion, returning JSON reports and visual scopes instead of relying on frame interpretations.
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables AI agents to validate and render branded videos from structured Video Specifications via the Model Context Protocol. Provides tools for health checks, video validation, and deterministic Remotion rendering.
      MIT

    TDQS

    B3.4/5.0

    Scored across 6 tools

    Disambiguation4/5

    Tools are mostly distinct by lifecycle stage: stg_account and stg_pricing cover license/commercials, stg_spend_report covers spend, stg_inspect_render measures technical quality, stg_detect_events finds edit points, and stg_qa_gate makes the final decision. stg_inspect_render and stg_detect_events both analyze video frames, but their outputs and stated uses differ clearly enough.

    Naming Consistency4/5

    All tools use the same stg_ prefix and snake_case casing, so the naming is predictable. Names mix verb_noun (stg_inspect_render, stg_detect_events) with noun_phrase labels (stg_account, stg_pricing), but that is a minor deviation rather than an inconsistent convention.

    Tool Count5/5

    Six tools are well-scoped for a render QA/guarding workflow, with each tool covering a distinct stage: account, pricing, spend, technical inspection, event detection, and final gate. The count is neither thin nor bloated.

    Completeness4/5

    The surface covers license status, pricing, spend tracking, technical render inspection, event detection, and a QA decision gate, which is a coherent lifecycle. Minor gaps remain around license activation/configuration and any explicit semantic-defect helper, but agents can work around these by supplying license state and semantic notes themselves.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues