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    • A
      license
      A
      quality
      B
      maintenance
      Enables AI coding agents to offload yes/no, multiple-choice, and scoring questions to TypeSafe's Jev, returning compact confidence-scored answers to save tokens and improve speed.
      1
      MIT
    • A
      license
      A
      quality
      C
      maintenance
      Enables coding or reasoning agents to request structured judgments from TypeSafe's Jev model at decision points, including choices, scores, claim verification, and code reviews, with probabilities and confidence returned as data.
      5
      4 npm
      MIT
    • A
      license
      A
      quality
      C
      maintenance
      Enables agents to query the TypeSafe Jev decision model for typed answers—yes/no, choice, and score questions—with calibrated probabilities, batched in a single request under a local context-budget guard.
      6
      MIT
    • A
      license
      B
      quality
      C
      maintenance
      Enables AI agents to obtain typed judgments from TypeSafe's Jev System One models, including yes/no probabilities, multiple-choice selections with distributions, and rubric-based scores, directly usable in code.
      5
      2
      AGPL 3.0
    • A
      license
      Not graded
      quality
      A
      maintenance
      Exposes three stdio tools (jev_ask, jev_rank, jev_check) that return typed judgments—yes/no, choice, and score answers with calibrated probabilities—instead of prose. It runs on an offline mock by default, resolving a live TypeSafe or OpenRouter credential only when configured, and reports all egress on stderr.
      66 npm
      Apache 2.0
    • A
      license
      Not graded
      quality
      A
      maintenance
      Lets MCP clients ask typed questions — yes/no, choice, or score — about structured state, including images, and receive calibrated probabilities from System One decision models such as TypeSafe Jev and Cloudflare Clef. Supports TypeSafe, OpenRouter, and local providers selectable per request, with input validation and cost reporting.
      0
      MIT

    TDQS

    A3.9/5.0

    Scored across 6 tools

    Disambiguation4/5

    The four question-type tools (choice, score, noul, box) each map to a distinct answer type, and the box description explicitly directs users to pair it with noul for presence checks. The only overlap is jev_decide, which bundles the same question types in a batch call; the descriptions clarify this, but an agent could still reasonably default to jev_decide for everything, blurring the boundary.

    Naming Consistency4/5

    All tools share a consistent 'jev_' prefix and snake_case, so the set reads predictably. Minor deviation: jev_list_models is verb_noun while jev_choice/score/noul/box are bare nouns naming question types, with jev_decide as a verb — mostly cosmetic.

    Tool Count5/5

    Six tools is well-scoped for a decision-model API: one per question type, a batch aggregator, and a model lister. No tool feels redundant or missing at this size.

    Completeness4/5

    The surface covers all four documented answer types plus batching and model discovery, which is the core of the domain. Minor gaps remain — no per-model capability introspection, usage/credit reporting, or error/diagnostic helper — but core workflows are covered.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues