AI agents call get_prompts_by_date to retrieve information from Learnlog without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool queries and retrieves stored prompts from a specific date without creating, modifying, deleting, or executing anything. It is a pure read operation with no side effects. The low severity reflects minimal risk—even if an agent queries all dates, the impact is informational only.
From the tool's definition Tool name 'get_prompts_by_date' and description indicating retrieval ('조회합니다' = 'retrieve/query') of historical prompts by date. No modification, deletion, or execution capability described.
Attacks that exploit this kind of access
특정 날짜에 했던 질문들을 조회합니다. It is categorised as a Read tool in the Learnlog MCP Server, which means it retrieves data without modifying state.
Register the Learnlog MCP server in PolicyLayer and add a rule for get_prompts_by_date: 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 Learnlog. Nothing to install.
get_prompts_by_date 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_prompts_by_date 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_prompts_by_date. 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_prompts_by_date is provided by the Learnlog MCP server (yujaeyun/learnlog-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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