Generate a session-wide security audit across all ingested fragments. Scans every fragment in the current session and returns an aggregated report showing: which fragments are most vulnerable, overall risk posture, finding distribution by category, and the single most important fix. Returns JSON ...
AI agents call security_report to retrieve information from Entroly Context Engine without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool generates reports by scanning ingested fragments and aggregating findings. It is purely analytical and read-only: it retrieves data and computes metrics (risk scores, counts by category) without side effects. No destructive, write, execute, or financial operations are performed. The low severity reflects that misuse would only expose security information, not compromise systems or data directly.
From the tool's definition Scans fragments and returns an aggregated security audit report showing vulnerabilities, risk scores, and findings distribution.
Risk signalsBulk/mass operation — affects multiple targets
Documented attack patterns abuse exactly the kind of access security_report gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Entroly Context Engine, and nothing reaches the server without passing your rules. This is the rule we recommend for security_report:
{
"version": "1",
"default": "deny",
"tools": {
"security_report": {}
}
}security_report is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Generate a session-wide security audit across all ingested fragments. Scans every fragment in the current session and returns an aggregated report showing: which fragments are most vulnerable, overall risk posture, finding distribution by category, and the single most important fix. Returns JSON with: - fragments_scanned, fragments_with_findings - critical_total, high_total, max_risk_score - most_vulnerable_fragment (fragment_id) - findings_by_category: {category: count} - vulnerable_fragments: sorted list by risk_score. It is categorised as a Read tool in the Entroly Context Engine MCP Server, which means it retrieves data without modifying state.
Register the Entroly Context Engine MCP server in PolicyLayer and add a rule for security_report: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Entroly Context Engine. Nothing to install.
security_report is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the security_report rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for security_report. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
security_report is provided by the Entroly Context Engine MCP server (juyterman1000/entroly). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Entroly Context Engine, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
Free to start. No card required.
52 Entroly Context Engine tools catalogued and risk-classified — across an index of 43,000+ MCP servers.