Eclipse Constellation MCP
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- AlicenseNot gradedqualityAmaintenanceEnables agents to route every action through a pre-execution gate that returns allow, escalate-to-human, or deny verdicts, backed by deterministic hard-rule blocks plus probabilistic gray-zone scoring, and to run a resumable orchestration loop covering routing, quality gates, stop-loss, acceptance, and escalation. Every decision, arbitration between conflicting agents, and human approval is written to an append-only hash-chained, Ed25519-signed ledger that can be postmortemed, calibrated, alert-scanned, and exported as offline verification bundles or OTel spans.1MIT
- 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
- FlicenseAqualityFmaintenanceProvides policy-based access control, incident tracking, and compliance monitoring to govern AI agent behavior. It enables organizations to enforce security rules and maintain audit trails by validating agent actions against trust levels and pattern-based policies.61-
- AlicenseNot gradedqualityBmaintenanceProvides permission gates and tamper-evident audit logging for AI agent tool executions, with declarative policies, consent ladders, and hash-chained verification.MIT
- 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
- AlicenseNot gradedqualityBmaintenanceProvides governed tools for evaluating proposed agent actions against policy, classifying risk, and creating structured governance and audit records, while pausing consequential steps for explicit human approval. This lets agentic workflows run under traceable, least-privilege control rather than unrestricted automation.MIT
TDQS
Scored across 5 tools
Each tool targets a distinct resource or operation: listing targets, listing receipts, probing a single target, snapshotting the fleet, and evaluating policy. The only potential overlap is fleet_snapshot vs probe_target, but the descriptions clearly separate aggregate snapshotting from single-target probing.
All names use snake_case, but the ordering is mixed: probe_target is verb_noun, receipt_list/target_list/policy_evaluate are noun_verb, and fleet_snapshot is noun_noun. It remains readable, but there is no single predictable pattern.
Five tools is well-scoped for an observe-only fleet observability and policy-evaluation server. Each tool earns its place, and the set is neither thin nor bloated.
The surface covers the core observe-only workflow: target listing, probing, fleet snapshotting, policy evaluation, and receipt auditing. Minor gaps exist, such as no direct snapshot-detail retrieval or target-declaration operation, but those are arguably outside the stated v0.1 scope.