aegis
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- AlicenseNot gradedqualityBmaintenanceDeterministic policy enforcement for AI agent tool calls. It evaluates every tool call against user-defined rules before execution, with no LLM in the authorization path.3MIT
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- AlicenseBqualityAmaintenanceA governance proxy for AI tools — every MCP/agent tool call is policy-gated, secret-redacted, and written to a hash-chained, offline-verifiable audit trail.13MIT

evav-gatewayofficial
AlicenseNot gradedqualityBmaintenanceGoverned MCP gateway that lets AI agents call tools with policy enforcement, prompt-injection screening, a kill-switch, and tamper-evident signed audit logs.Apache 2.0- AlicenseAqualityCmaintenanceRuntime policy enforcement for AI agents. Evaluate every agent action against your organization's policies before execution, with observe and enforce modes.11MIT

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.12 npmMIT
TDQS
Scored across 5 tools
Tools have distinct purposes, but 'check_risk' and 'evaluate_action' overlap conceptually as both assess actions, with 'check_risk' being lighter. This could cause confusion, though descriptions help differentiate.
All tool names follow a consistent 'verb_noun' pattern in snake_case (e.g., 'check_risk', 'evaluate_action', 'get_policy'), ensuring clarity and predictability.
With 5 tools covering risk checking, single evaluation, batch evaluation, policy retrieval, and policy update, the count is well-scoped for governance policy management.
The tool set covers all core operations: quick risk checks, detailed evaluations (individual and batch), and policy management (view and update), with no obvious gaps.