aggrete
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AlicenseNot gradedqualityCmaintenanceEnforces policy controls for AI agents, including spend limits, action approvals, kill switch, scoped credentials, dry-run diffs, loop prevention, and auditable hash-chained logs.MIT- AlicenseBqualityBmaintenanceA 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

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.48 npmMIT- AlicenseAqualityAmaintenanceLocal zero-trust permission gateway for AI agents. Enforces policy-based tool authorization, human approvals, scoped permissions, and cryptographically verifiable audit logs.45Apache 2.0

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
Blekline MCP Serverofficial
AlicenseAqualityAmaintenanceProvides AI ingress governance by masking prompts, classifying risk, and enforcing tool policies before agent calls reach model providers or sandboxes.61AGPL 3.0
TDQS
Scored across 12 tools
Each tool maps to a distinct resource or action: HR person-level lookups, finance aggregate views, ops drafting, external web read/write, and policy meta-tools. The domain prefixes and bracketed categories make it easy to tell timecard, leave balance, headcount plan, budget roles, and pay band apart.
All names use a domain__snake_case prefix, which provides a consistent overall structure. However, the action part is mixed: several tools are bare noun phrases (timecard, headcount_plan, pay_band), while others are verb phrases (read_public_post, post_note, start_here) or single verbs (check, scenarios).
Twelve tools is well within the ideal range, and each tool earns its place: data sources across HR, finance, ops, and external web, plus guidance and policy-checking tools. The count is large enough to demonstrate multiple policy scenarios without becoming a sprawling surface.
For a policy-decision demo, the surface is complete: start_here and scenarios orient the agent, check lets it test policy decisions before acting, and the data/web tools cover the described policy cases including redaction, comparing colleagues, individual pay, combination refusals, and the prompt-injection shield. No obvious dead-end workflow or missing operation exists for the stated purpose.