memory_retrieve_records
AI agents call memory_retrieve_records 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 verb 'retrieve' paired with 'records' indicates querying or fetching data without modification. Despite empty description lowering confidence slightly, the naming convention strongly suggests a Read operation with low severity (data retrieval only). Classification as Read rather than higher categories is appropriate absent evidence of write, execute, delete, or financial operations.
From the tool's definition Tool name 'memory_retrieve_records' indicates data retrieval via the 'retrieve' verb, consistent with Read operations. No description provided to suggest side effects or modifications.
Attacks that exploit this kind of access
memory_retrieve_records. 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_retrieve_records: 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_retrieve_records 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_retrieve_records 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_retrieve_records. 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_retrieve_records 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.