Skip to main content
Glama

Related Servers

Alternatives to jev-mcp

No user-submitted related servers found.

    Related Servers

    • 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
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables typed decisions (yes/no, choice, score) with transparent preflight checks, honest confidence reporting, and health monitoring.
      147 npm
      Apache 2.0
    • 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
      C
      maintenance
      Enables AI assistants to obtain calibrated probabilities, option picks, scale scores, and multi-question judgments for batches of short text, returning numeric results for sorting, filtering, and counting.
      MIT

    TDQS

    A4.1/5.0

    Scored across 5 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose: listing models, picking one from a set, scoring on a scale, yes/no checking, and batching multiple independent questions. The descriptions clarify boundaries (e.g., jev_check is specifically for yes/no, while jev_classify handles arbitrary options). No overlap that would cause an agent to misselect.

    Naming Consistency4/5

    All tools share the consistent 'jev_' prefix, but the second part is mostly verbs (classify, ask, score, check) except for 'models' which is a noun. This is a minor deviation from a strict verb pattern, but still predictable and readable.

    Tool Count5/5

    Five tools is well-scoped for a classification/scoring API. Each tool earns its place and covers distinct operations without redundancy. This is within the ideal 3-15 range and appropriate for the server's purpose.

    Completeness5/5

    The surface covers the core operations: model discovery, single-label classification, ordered scoring, binary checks, and batched multi-question inference. There are no obvious gaps for the apparent domain; agents can perform all necessary workflows without workarounds.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues