최근 에러 로그를 조회합니다.
AI agents call get_errors to retrieve information from MCP Remote Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
The tool retrieves error logs for inspection purposes. This is a read-only operation that queries existing data without modifying, deleting, or executing any operations. The context of a remote server managing Gmail, OpenAI, and Salesforce interactions suggests this retrieves diagnostic information.
From the tool's definition Tool name 'get_errors' and description '최근 에러 로그를 조회합니다' (retrieves recent error logs) indicate a query/retrieval operation with no side effects.
Attacks that exploit this kind of access
최근 에러 로그를 조회합니다. It is categorised as a Read tool in the MCP Remote Server MCP Server, which means it retrieves data without modifying state.
Register the MCP Remote Server MCP server in PolicyLayer and add a rule for get_errors: 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 MCP Remote Server. Nothing to install.
get_errors 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 get_errors 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 get_errors. 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.
get_errors is provided by the MCP Remote Server MCP server (sunnylabtv-crypto/ai_mcp_fastmcp_remote-public). 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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