Cyan Eclipse Apply MCP
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- FlicenseNot gradedqualityAmaintenanceThe Control Plane for Autonomous AI Enforce policy before execution, require human approvals where risk demands it, and keep a full audit trail — from first action to final result.510-
- FlicenseNot gradedqualityBmaintenanceEnables AI coding agents to request bounded GitHub changes through a default-deny policy, independent human approval, one-time execution with a GitHub App credential, and verifiable receipts.1-
- AlicenseAqualityAmaintenanceGates 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.4529 PyPI15AGPL 3.0
- AlicenseNot gradedqualityDmaintenanceDeterministic AI code review with audit records, providing stack-specific rulesets and governance for coding agents.5MIT

ERDL Guardofficial
AlicenseNot gradedqualityAmaintenanceEnforces deterministic policy decisions on AI agent tool calls, supporting allow, deny, correct, escalate, and human review actions with verifiable audit receipts.10 npmMIT- AlicenseNot gradedqualityBmaintenanceEnables 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.Apache 2.0
TDQS
Scored across 13 tools
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.
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.
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.
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.