GetAHOReferenceStore
AI agents call GetAHOReferenceStore 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 'Get' prefix is a standard convention for read operations that fetch existing data. In AWS services, GetX operations typically retrieve configuration, metadata, or data stores without modification. The empty description prevents higher confidence, but the name alone provides reasonable evidence for Read classification. No indicators of modification, deletion, or execution are present.
From the tool's definition Tool name 'GetAHOReferenceStore' follows the 'Get' prefix pattern, which conventionally retrieves or queries data without side effects. The empty description limits confidence, but the naming convention strongly suggests a retrieval operation.
Attacks that exploit this kind of access
GetAHOReferenceStore. 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 GetAHOReferenceStore: 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.
GetAHOReferenceStore 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 GetAHOReferenceStore 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 GetAHOReferenceStore. 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.
GetAHOReferenceStore 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.