AI agents call get_list to retrieve information from Interview without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves or queries a todo list by name. It reads existing data without creating, modifying, deleting, or executing operations. This is a straightforward Read operation with minimal risk—an AI agent using it cannot cause harm, only view task list contents.
From the tool's definition Tool name 'get_list' and description 'Obter, ver la lista con nombre...' (get/view/retrieve a list) indicates data retrieval with no modification or deletion.
Attacks that exploit this kind of access
Obter, ver la lista con nombre ‘Prueba’. It is categorised as a Read tool in the Interview MCP Server, which means it retrieves data without modifying state.
Register the Interview MCP server in PolicyLayer and add a rule for get_list: 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 Interview. Nothing to install.
get_list 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_list 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_list. 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_list is provided by the Interview MCP server (matilarrazabal/mcp-server-interview). 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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