Retrieve all resources related to a specific patient using the FHIR $patient-everything operation
AI agents call patient_everything 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.
This tool retrieves healthcare data (FHIR resources) for a specified patient. It performs no modifications, deletions, or external actions—it is purely a data retrieval operation. Even though it accesses sensitive patient health information, the risk category concerns what the tool DOES (read/query), not data sensitivity.
From the tool's definition Tool name 'patient_everything' and description 'Retrieve all resources related to a specific patient' indicates a read-only query operation.
Attacks that exploit this kind of access
Retrieve all resources related to a specific patient using the FHIR $patient-everything operation. 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 patient_everything: 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.
patient_everything 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 patient_everything 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 patient_everything. 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.
patient_everything 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.