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Alternatives to General Decision MCP

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    Related Servers

    • A
      license
      Not graded
      quality
      D
      maintenance
      Enables AI clients to run read-only yes/no checks, classifications, and ordered scores with probabilities, supporting triage, routing, sentiment, and lead-fit decisions through hosted or self-hosted MCP tools.
      41 npm
      AGPL 3.0
    • A
      license
      A
      quality
      C
      maintenance
      Enables searching fictional policy documents and assessing expense claims against deterministic rules via MCP, returning structured assessments and citations without an approval tool.
      2
      MIT
    • A
      license
      A
      quality
      B
      maintenance
      Enables AI clients to assess infrastructure-as-code deployment risk, generate incident response runbooks, review CI/CD delivery readiness, and calculate SLO error budgets through structured advisory tools. Runs locally over stdio without cloud credentials or external API calls, returning guidance for platform work, pull request review, and production readiness checks.
      12
      185 npm
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Provides local, read-only recommendations for reasoning effort, retry or escalation guidance, and integration boundaries, enabling compatible desktop apps to suggest effort levels without modifying the active conversation.
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Hosts an advisory review layer that compares proposed agent tool calls and explanations against developer-owned policy and available evidence before a human decision, returning grounded approval recommendations over MCP.
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables AI assistants to securely query a synthetic health-insurance claims database through read-only, parameterized tools for claims, member histories, coverage rules, and year-to-date summaries. Includes API-key authentication, per-tool authorization, rate limiting, untrusted-text sanitization, and fail-closed audit logging.
      MIT

    TDQS

    A3.6/5.0

    Scored across 9 tools

    Disambiguation4/5

    Each tool covers a distinct decision-support function (screen, classify, rank, verify, review, gate, risk, decide, coding loop), and the descriptions clarify the boundaries fairly well. The main friction is the review/gate pair and the verify/decide/rank cluster, which sit close together conceptually and require reading descriptions to separate.

    Naming Consistency4/5

    All names share a consistent jev_ prefix with short descriptive suffixes, which is highly predictable. Minor deviation: jev_coding_loop is a compound noun phrase while the rest are single verbs, but it remains readable and unlikely to confuse.

    Tool Count5/5

    Nine tools is well within a comfortable scope for a decision-support suite, and each maps to a different stage of the pipeline rather than duplicating another. No tool appears redundant or trivially filler.

    Completeness4/5

    The surface covers the full advisory pipeline from screening and classification through ranking, verification, review, gating and final decision, which is solid coverage for the stated purpose. Minor gaps exist around recording/auditing decisions or retrieving prior judgments, but agents can work around these by passing context in.

    Maintenance

    ActivityMaintained
    ResponsivenessOutdated data