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Alternatives to feellab-mcp

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    Related Servers

    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables AI clients to track first-person weapon bugs as measurable cases, run a game's own probe to gather metrics and screenshots, and compare candidate fixes against a baseline before a change is approved. It also reads posed weapon parts dumps, rebuilds animation clips in Blender, and validates probe contracts.
      1
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Validates a built HTML5 game against CrazyGames portal rules — build checks, an emulated SDK v3 session, and frame/layout audits — returning dense JSON lists of failures, warnings and rules it could not exercise instead of screenshots or ad-hoc Playwright scripts. It emulates the portal entirely in-process (no server or port) using the game's own Playwright/Chromium, with a small per-game scenario file driving gameplay steps.
      GPL 3.0
    • A
      license
      Not graded
      quality
      A
      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.
      44 PyPI
      MIT
    • A
      license
      A
      quality
      C
      maintenance
      Loudness compliance verdicts against formal broadcast standards (EBU R128, ATSC A/85). Measures audio/video with ffmpeg and returns a pass/fail verdict with exact deltas and remediation parameters.
      2
      97 PyPI
      1
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables inspecting ONNX models, probing the deployment environment, and benchmarking inference latency and FPS either locally or on remote SSH targets such as Linux workstations and Jetson devices. Benchmark results are validated against FPS, P95 latency and model-size constraints to produce PASS/FAIL deployment gates.
      47 npm
      MIT

    TDQS

    C2.9/5.0

    Scored across 6 tools

    Disambiguation4/5

    The three list_* tools clearly target distinct resources (checklist, scenes, metrics), and check_timeline vs judge_folder differ in scope (single file validation vs folder-level judging). Minor overlap between check_timeline and judge_folder since both involve evaluation, but descriptions separate them adequately.

    Naming Consistency4/5

    Five of six tools follow a clean verb_noun pattern (list_checklist, list_scenes, list_metrics, check_timeline, judge_folder). gate_rows deviates in form, but it's a single minor exception rather than a systematic inconsistency.

    Tool Count5/5

    Six tools is well-scoped for an evaluation/QA framework: three listing tools plus three analysis tools. Each tool earns its place with no redundancy or padding.

    Completeness4/5

    The surface covers the inspection lifecycle: enumerating resources (checklist, scenes, metrics), validating timelines, judging folders, and emitting gate rows. The toolset is read/analysis-oriented, so authoring or updating checklists/metrics/scenes is absent, but that appears intentional for this domain.