memory_list_actors
AI agents call memory_list_actors to retrieve information from Amazon SageMaker AI MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
The name pattern 'memory_list_actors' is characteristic of a Read operation (list/query). While the description is empty which lowers confidence, the '_list' suffix strongly indicates data retrieval without modification or destructive capability. No write, execution, or deletion semantics are evident from the name. Classified as Read with low severity due to minimal blast radius from querying actor information.
From the tool's definition Tool name 'memory_list_actors' suggests querying or listing actors from memory storage; the '_list' naming convention indicates a retrieval operation with no side effects.
Attacks that exploit this kind of access
memory_list_actors. It is categorised as a Read tool in the Amazon SageMaker AI MCP Server MCP Server, which means it retrieves data without modifying state.
Register the Amazon SageMaker AI MCP Server MCP server in PolicyLayer and add a rule for memory_list_actors: 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 Amazon SageMaker AI MCP Server. Nothing to install.
memory_list_actors 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 memory_list_actors 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 memory_list_actors. 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.
memory_list_actors is provided by the Amazon SageMaker AI MCP Server MCP server (awslabs.sagemaker-ai-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.