get_execution_plan
AI agents call get_execution_plan 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 tool name indicates a retrieval action ('get') without modifying data. In AWS SageMaker context, execution plans are typically read-only artifacts that describe what would run rather than causing side effects. Without a description, confidence is moderately lowered, but the verb 'get' and noun 'plan' most naturally map to the Read category rather than Execute, despite potential ambiguity.
From the tool's definition Tool name 'get_execution_plan' suggests retrieval of an execution plan (likely from SageMaker), which aligns with a query/read operation rather than execution or modification. However, the description is empty, limiting confidence.
Attacks that exploit this kind of access
get_execution_plan. 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 get_execution_plan: 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.
get_execution_plan 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 get_execution_plan 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 get_execution_plan. 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.
get_execution_plan 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.