Adversarial quality review of any AI-generated output. An independent reviewer assumes the author made mistakes and actively looks for problems. Returns structured verdict (PASS/FAIL/CONDITIONAL_PASS), score (0-100), categorized issues with severity, and evidence-based checklist. Works for any ou...
AI agents call review_output to retrieve information from Agentdesk without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This is a pure read/analysis operation. The tool takes AI-generated output as input and returns structured feedback (verdict, score, issues, checklist). It has no side effects—it does not execute code, modify data, delete anything, or commit financial actions. Even though it performs adversarial/critical analysis, that analysis itself is non-destructive information retrieval and reporting.
From the tool's definition Tool performs 'review' and 'returns structured verdict...score...issues' — it analyzes and retrieves quality assessment data without modifying, executing, or deleting anything.
Attacks that exploit this kind of access
Adversarial quality review of any AI-generated output. An independent reviewer assumes the author made mistakes and actively looks for problems. Returns structured verdict (PASS/FAIL/CONDITIONAL_PASS), score (0-100), categorized issues with severity, and evidence-based checklist. Works for any output type: code, content, summaries, translations, data extraction, etc. It is categorised as a Read tool in the Agentdesk MCP Server, which means it retrieves data without modifying state.
Register the Agentdesk MCP server in PolicyLayer and add a rule for review_output: 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 Agentdesk. Nothing to install.
review_output 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 review_output 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 review_output. 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.
review_output is provided by the Agentdesk MCP server (rih0z/agentdesk-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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