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Cyan Eclipse Apply MCP

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      Gates every AI agent tool call against your policy before it runs, then writes a hash-chained, tamper-evident record of what was decided and what happened. Anyone can verify that record offline, without your software, your systems or any key you hold.
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      Enables AI agents to perform safe, auditable Kubernetes write actions such as scaling, restarting and rolling back Deployments through a plan → policy-check → approve → apply → audit flow, with server-side guards, single-use plans and scoped RBAC. Read-only planning and Prometheus SLO/alert tools support the same guarded workflow.
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    TDQS

    B3.3/5.0

    Scored across 13 tools

    Disambiguation5/5

    Each tool targets a distinct stage or control: search vs. match vs. prepare vs. approve/reject are clearly separated, and policy get/set, pause/resume, tracker vs. receipts are also easy to distinguish. No two tools appear to do the same thing, and the descriptions reinforce the boundaries.

    Naming Consistency4/5

    Nearly all tools follow a consistent snake_case verb_noun pattern (search_jobs, prepare_application, set_policy). The global pause/resume actions omit an explicit noun, which is a minor but reasonable deviation.

    Tool Count5/5

    With 13 tools, the set is well-scoped for a job-application workflow that includes search, matching, preparation, approval, receipts, policy, and safety controls. Each tool appears to earn its place, and the count is comfortably within the ideal range.

    Completeness4/5

    The core lifecycle is covered: search, match, prepare, approve/reject, track, receipt, policy, pause/resume, and data purge. Minor gaps remain, such as no standalone tool to view or update a profile after ingestion, and no explicit job-detail or cached-job listing tool.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues