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Alternatives to jev-mcp

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    Related Servers

    • A
      license
      A
      quality
      B
      maintenance
      Enables agents to verify claims against cited evidence, screen content for prompt injection and relevance before reading it, and rank candidates by meaning, all with calibrated probability verdicts.
      3
      45
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables MCP-aware clients to use a reliable multi-agent tool system with tiered model routing, schema-validated decisions, and graceful tool degradation, allowing small models to handle complex tasks.
      MIT
    • A
      license
      A
      quality
      C
      maintenance
      Enables agents to get fast, calibrated probabilistic answers from Jev (Typesafe AI) to yes/no, scale, or choice questions about provided material, without using a generative model.
      1
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Provides 8 MCP tools for deterministic, read-only reasoning: intake, routing, planning, rubric, sweep checklist, verdict gate, reflection, and evaluation. It forces scope locks, disconfirmation-first plans, blind-spot sweeps, and evidence-gated verdicts.
      Apache 2.0
    • A
      license
      A
      quality
      C
      maintenance
      Enables frontier coding agents to delegate routine probabilistic judgments to TypeSafe Jev, providing calibrated triage signals for failures, attempts, completion, context ranking, findings, risk, and generic evidence-grounded questions.
      7
      MIT
    • F
      license
      Not graded
      quality
      C
      maintenance
      MCP server that enforces governance on agentic decisions with auditable evidence records, providing tools for understanding, calibrating confidence, and navigating handoffs based on policy.
      1
      -

    TDQS

    A4.7/5.0

    Scored across 6 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose: evaluate is a general-purpose judgment batcher, rank scores candidate texts, verify checks claims against evidence, gate_action flags risky actions, next_step guides agent loops, and list_models manages model selection. There is no meaningful overlap even among the three decision-oriented tools because they operate on different inputs and produce different verdict types.

    Naming Consistency5/5

    All tools share a consistent lowercase snake_case pattern with the jev_ prefix followed by an imperative verb or verb_noun (evaluate, rank, verify, gate_action, next_step, list_models). The convention is uniform and predictable, making it easy for an agent to infer tool purpose from the name alone.

    Tool Count5/5

    Six tools is a well-scoped set for a judgment-model server: a general evaluation API plus four specialized judgment variants and one meta-tool for model listing. Each tool earns its place and there is no sign of bloat or redundancy.

    Completeness5/5

    The tool surface covers the full lifecycle of using a calibrated judgment model: general evaluation, ranking, verification, action pre-flight, next-step guidance, and model discovery. The descriptions explicitly frame jev_evaluate as the catch-all, with the other tools as targeted specializations, so no obvious capability gap remains.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues