List the current user's favorite recipes.
AI agents call list_self_favorites to retrieve information from Mealie MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves and queries favorite recipe data belonging to the current user with no side effects, creation, modification, or deletion of data. It is a simple read operation with minimal risk even if misused by an AI agent.
From the tool's definition Tool name 'list_self_favorites' and description 'List the current user's favorite recipes' clearly indicate data retrieval with no modifications.
Attacks that exploit this kind of access
List the current user's favorite recipes. It is categorised as a Read tool in the Mealie MCP Server MCP Server, which means it retrieves data without modifying state.
Register the Mealie MCP Server MCP server in PolicyLayer and add a rule for list_self_favorites: 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 Mealie MCP Server. Nothing to install.
list_self_favorites 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 list_self_favorites 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 list_self_favorites. 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.
list_self_favorites is provided by the Mealie MCP Server MCP server (nikopol666/mealie-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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