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Alternatives to super-loop-mcp

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    • A
      license
      A
      quality
      B
      maintenance
      A machine-enforced workflow protocol for AI coding agents that eliminates false progress. Tasks require verifiable evidence to close, while decisions, assumptions, and open questions persist in a versioned reasoning graph for seamless agent handoffs. Designed to keep deep, long-running projects coherent across sessions, agents, and months.
      39
      26
      AGPL 3.0
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables creative software agents to operate within bounded, temporary workcells, handling scored objectives through autonomous inspect, execute, capture, and correct loops while producing evidence-backed receipts for human review.
      Apache 2.0
    • A
      license
      Not graded
      quality
      C
      maintenance
      Provides coding agents with durable, cross-session lessons-learned memory, enforcing that success or failure verdicts can only come from human approval, human correction, or objective metrics—never from the agent itself.
      Apache 2.0
    • A
      license
      A
      quality
      B
      maintenance
      Enforces disciplined programming practices by requiring AI assistants to audit their work and produce verified outputs at each phase of development, following structured workflows for refactoring, feature development, and testing.
      20
      35 npm
      12
      MIT

    TDQS

    C2.9/5.0

    Scored across 29 tools

    Disambiguation4/5

    Tools have mostly distinct purposes, with detailed descriptions clarifying roles. However, some tools like 'continue_run' and 'request_next_phase' could be confused without careful reading, slightly reducing disambiguation.

    Naming Consistency3/5

    Naming is predominantly snake_case but mixes verb-first (e.g., 'artifact_record') and noun-first (e.g., 'campaign_status') patterns. Some compound names are awkward, and there is an alias ('loop_next') that adds redundancy.

    Tool Count2/5

    29 tools is high for the domain; while each tool has a specific role, the number exceeds the typical 3-15 range and is above 25, making the surface heavy and potentially overwhelming for agents.

    Completeness4/5

    The tool set covers the full campaign lifecycle: initialization, benchmarking, hypothesis testing, looping, human review, verification, and reporting. Minor gaps exist, such as no direct artifact listing, but overall it is comprehensive.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues