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Alternatives to jev-mcp

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    Related Servers

    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables AI agents to submit a state and typed questions and receive structured judgments such as yes/no probabilities, named choices, and rubric scores, with no text generation or parsing.
      MIT
    • A
      license
      Not graded
      quality
      A
      maintenance
      Enables coding agents to send natural language and JSON application state to a judge tool that returns typed judgments (yes/no probability, choice, or score) with model and usage metadata, and fails gracefully without blocking workflows.
      12 npm
      5
      Apache 2.0
    • A
      license
      Not graded
      quality
      A
      maintenance
      Enables typed decisions (yes/no, choice, score) with transparent preflight checks, honest confidence reporting, and health monitoring.
      65 npm
      Apache 2.0
    • A
      license
      A
      quality
      C
      maintenance
      Enables agents to call typed code-review and content-moderation decision tools, returning structured verdicts, probabilities, and confidence-gated actions.
      2
      MIT

    TDQS

    A4.1/5.0

    Scored across 3 tools

    Disambiguation5/5

    The three tools split cleanly along orthogonal axes: jev_ask (one state, many questions), jev_batch (many items, one question set), and jev_status (health/housekeeping, no judgments). Descriptions explicitly include 'Not for' clauses that steer agents away from the wrong tool (e.g., 'For many items use jev_batch', 'Not for several questions about one state').

    Naming Consistency5/5

    All names follow a single predictable pattern: the shared `jev_` prefix plus a short snake_case noun (ask, batch, status). No mixed conventions or verb-style drift.

    Tool Count4/5

    Three tools is on the lean side, but each occupies a distinct role (single judgment, batched judgment, meta/ops) and nothing feels redundant. A slightly larger surface (e.g., a recipe-listing tool) might round it out, but the current set is well-scoped.

    Completeness4/5

    The surface covers the full judgment lifecycle: single calls, concurrent batch calls, offline validation, threshold presets, feedback labeling, and calibration. Minor gaps exist — no dedicated tool to list or inspect past decisions/history, and recipe discovery is only implied through the `recipe` arg rather than being a first-class operation.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues