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Alternatives to aibvf-mcp

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    • A
      license
      A
      quality
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      maintenance
      Enables AI assistants and agents to vet a stablecoin or x402 payment before it is sent, returning an approve, hold, or reject decision from instant rule checks and an optional locally-run risk model. Fails closed, so malformed input or an unreachable model yields a hold rather than an approval.
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      Apache 2.0
    • A
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      quality
      A
      maintenance
      Enables coding agents to scout, rank, and preflight software work before implementation, returning evidence-backed ACT, VERIFY, or SKIP decisions for issues and pull requests.
      26 npm
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    • A
      license
      Not graded
      quality
      A
      maintenance
      Enables typed decisions (yes/no, choice, score) with transparent preflight checks, honest confidence reporting, and health monitoring.
      65 npm
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    • A
      license
      A
      quality
      D
      maintenance
      A pre-action risk gate for AI agents. Your agent calls the forecast tool before any irreversible action — send email, run SQL, make a payment, delete a file — and gets a risk score (0–100) and a GO / CONFIRM / STOP verdict in a few seconds.
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      20 npm
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    • F
      license
      Not graded
      quality
      C
      maintenance
      Provides AI agents with personalized timing intelligence by scoring decisions (0-100) against a user's energy profile and the Five Elements framework, enabling optimal scheduling for actions like trip planning, product launches, and meetings.
      1
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    • A
      license
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      quality
      C
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      Scores proposed tool calls with a decision model before execution, returning allow, ask, or block decisions with safety, production, and prompt-injection probabilities so agents can gate risky actions and fail closed on scorer errors.
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    TDQS

    A4.8/5.0

    Scored across 13 tools

    Disambiguation5/5

    Every tool targets a distinct action+resource, and descriptions explicitly route between neighbors (e.g. 'use score_initiative when fields are known, assess_ai_initiative for plain language', 'use score_portfolio for several initiatives'). The assess/score_initiative pair both return verdicts but is cleanly separated by input state, and map_to_taxonomy vs list_taxonomy vs validate_portfolio vs assemble_portfolio are unambiguous.

    Naming Consistency5/5

    All 13 names are snake_case with a consistent verb_noun pattern (assess_ai_initiative, score_portfolio, validate_portfolio, map_to_taxonomy, get_benchmark, infer_readiness, diagnose_process). No mixed conventions or vague verbs like process/run/execute.

    Tool Count5/5

    13 tools is well within the sweet spot and each earns its place: lookup (taxonomy, benchmark), assembly/validation, scoring (single/portfolio), sequencing, and diagnosis/remediation are all distinct functions. No redundant or filler tools.

    Completeness5/5

    The surface covers the full lifecycle: free-text mapping, taxonomy listing, portfolio assembly and validation, single/portfolio scoring, wave sequencing, improvement planning, pace-layer cost, benchmark lookup, process diagnosis and readiness inference. No obvious dead ends for the stated domain.

    Maintenance

    ActivityMaintained
    ResponsivenessUnresponsive