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Alternatives to aibvf-mcp

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    • A
      license
      Not graded
      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.
      64
      2
      MIT
    • 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.
      1
      523
      MIT
    • F
      license
      Not graded
      quality
      D
      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
      -
    • A
      license
      Not graded
      quality
      F
      maintenance
      Quantitative governance gate for AI agents. Six gates (risk, profit, novelty, complexity, quality, utility) return PROCEED/PAUSE/HALT/ESCALATE with confidence scores and hash-chained, tamper-evident audit trails. Generates NIST AI RMF and EU AI Act Annex IV artifacts. 10 MCP tools; local stdio and hosted Streamable HTTP with a free tier.
      MIT
    • A
      license
      B
      quality
      B
      maintenance
      Enables AI clients to analyze software test results and defect records, producing explainable GO, CONDITIONAL_GO, or NO_GO release-readiness recommendations with deterministic risk scoring, failed-test retrieval, defect hotspot ranking, and regression test planning.
      4
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      A pre-action authorization server for AI agents that classifies tool calls into 14 intent categories, scores risk 0-100, and produces deterministic allow/deny/ask decisions with full audit trail.
      MIT