Analyze code for issues, security vulnerabilities, and improvement suggestions.
AI agents call code_review to retrieve information from AgentSkills MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool reads and analyzes submitted code, producing a report of findings. It does not execute the code, modify any state, or have external side effects — it is purely analytical (read/inspect).
From the tool's definition Analyze code for issues, security vulnerabilities, and improvement suggestions
Attacks that exploit this kind of access
Analyze code for issues, security vulnerabilities, and improvement suggestions. It is categorised as a Read tool in the AgentSkills MCP Server MCP Server, which means it retrieves data without modifying state.
Register the AgentSkills MCP Server MCP server in PolicyLayer and add a rule for code_review: 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 AgentSkills MCP Server. Nothing to install.
code_review 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 code_review 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 code_review. 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.
code_review is provided by the AgentSkills MCP Server MCP server (primadonna-gpters/agentskills-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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