cost-optimization
AI agents call cost-optimization 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.
Without a description, we cannot determine the exact behavior. The name 'cost-optimization' most naturally suggests a tool that analyzes or reports on cost metrics (Read operation), but it could theoretically trigger resource scaling or termination actions if it automatically optimizes infrastructure. Given the ambiguity and that SageMaker is an execution platform, this is classified as Read with low confidence.
From the tool's definition Tool name is 'cost-optimization' with an empty description. The name suggests reporting or analysis of cost data rather than direct modification or execution, but the lack of description creates ambiguity.
Attacks that exploit this kind of access
cost-optimization. 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 cost-optimization: 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.
cost-optimization 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 cost-optimization 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 cost-optimization. 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.
cost-optimization 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.