AI agents call get_favorite_categories to retrieve information from Kwork without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves user preference data (favorite categories) without modifying, deleting, or executing any operations. It is a straightforward Read operation with minimal security risk.
From the tool's definition Tool name 'get_favorite_categories' and description 'Получить список избранных категорий текущего пользователя' (Get list of favorite categories of current user) — clearly a retrieval operation with no side effects.
Attacks that exploit this kind of access
Получить список избранных категорий текущего пользователя. It is categorised as a Read tool in the Kwork MCP Server, which means it retrieves data without modifying state.
Register the Kwork MCP server in PolicyLayer and add a rule for get_favorite_categories: 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 Kwork. Nothing to install.
get_favorite_categories 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_favorite_categories 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_favorite_categories. 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_favorite_categories is provided by the Kwork MCP server (simonether/kwork-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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