list_config_check_definitions
AI agents call list_config_check_definitions 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 'list_' prefix strongly suggests enumeration of configuration check definitions without modification. This is a non-destructive read operation. Confidence is moderate (0.72) rather than high due to the empty description, which leaves some ambiguity about what data is returned and whether there are side effects, but the naming convention is a reliable indicator of read-only behavior.
From the tool's definition Tool name 'list_config_check_definitions' indicates a listing/retrieval operation with 'list' prefix, typical of read-only queries. No description provided to confirm scope.
Attacks that exploit this kind of access
list_config_check_definitions. 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_config_check_definitions: 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_config_check_definitions 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_config_check_definitions 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_config_check_definitions. 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_config_check_definitions 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.