list_applications
AI agents call list_applications 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.
Despite the empty description, the naming convention 'list_*' is a strong indicator of a read operation that retrieves information about applications without modification. This is consistent with AWS SageMaker's data retrieval patterns. Confidence is lowered from higher due to the lack of description, but the tool name itself provides sufficient evidence for classification as a Read operation with low risk.
From the tool's definition Tool name 'list_applications' combined with sibling tools like 'aggregate', 'analyze_batch_translation_errors', and other read-pattern tools suggests a listing/query operation. The name follows standard read operation patterns (list_*).
Attacks that exploit this kind of access
list_applications. 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 list_applications: 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.
list_applications 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 list_applications 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 list_applications. 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.
list_applications 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.