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    Related Servers

    • 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
    • A
      license
      Not graded
      quality
      A
      maintenance
      Exposes local MCP tools for typed, calibrated LLM decisions — gating risky actions, yes/no judging, classification, ordinal rating, and multi-question decisions in one pass. Runs on the model Devin's CLI already provides via ACP (or a fully offline local backend), so agents get calibrated confidence with no extra API key, no Ollama, and no waitlist.
      51 PyPI
      6
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables coding agents to query a locally running Kev decision model through MCP tools, returning calibrated probabilities for typed questions such as yes/no, choice, and score.
      Apache 2.0
    • A
      license
      A
      quality
      A
      maintenance
      Enables coding agents to query a local Clef decision model for structured probability distributions over allowed answers, returning typed decisions for next actions, confidence scores, and yes/no questions through MCP stdio.
      1
      46 npm
      7
      Apache 2.0
    • A
      license
      Not graded
      quality
      A
      maintenance
      Exposes three stdio tools that answer typed questions — yes/no, choice, and score — with calibrated probabilities instead of prose, letting any MCP host query a judgment model without a chat interface. Runs fully offline on a synthetic mock by default, with named, per-feature egress disclosure and redaction only when a live provider is explicitly configured.
      66 npm
      Apache 2.0
    • A
      license
      A
      quality
      B
      maintenance
      Provides agents with MCP tools for typed probabilistic decisions—Choice, Noul, and Score—with calibrated probabilities and abstention, using local OpenAI-compatible models via LM Studio logprobs.
      4
      1
      MIT

    TDQS

    A4.1/5.0

    Scored across 1 tool

    Disambiguation5/5

    With only a single tool in the set, there is no possibility of tool-to-tool confusion; the agent has exactly one entry point. The internal mode parameter (choice/score/noul) is well documented, so even the intra-tool variation is clear.

    Naming Consistency5/5

    One tool named 'advise' is trivially consistent — a clean, lowercase verb with no competing conventions to clash against. There is no naming pattern to violate.

    Tool Count4/5

    A single 'advise' tool with modes is a defensible design for wrapping one decision model, since the modes are variants of the same operation rather than genuinely distinct resources. It is slightly thin, but not a mismatch with the server's stated scope.

    Completeness3/5

    The core advisory surface (categorical choice, ordered score, and binary proposition) is covered, but there is no way to report outcomes back for calibration, request explanations, or batch multiple questions. These are notable gaps for a decision-support domain, though agents can still function.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues