Use this as the primary tool to list the log buckets in a Google Cloud project. Log buckets are containers that store and organize your log data. This tool is useful for understanding how your logs are stored and for managing your logging configurations.
AI agents call list_buckets to retrieve information from Storage without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool performs a read-only query operation that retrieves information about existing log buckets. It has no side effects, does not modify data, and poses minimal risk even if misused by an AI agent, as it only exposes bucket metadata that an authenticated user would typically have permission to view.
From the tool's definition Tool lists log buckets in a Google Cloud project with no modification capability. Description states 'list the log buckets' and emphasizes understanding storage without any create, update, or delete operations.
Attacks that exploit this kind of access
Use this as the primary tool to list the log buckets in a Google Cloud project. Log buckets are containers that store and organize your log data. This tool is useful for understanding how your logs are stored and for managing your logging configurations. It is categorised as a Read tool in the Storage MCP Server, which means it retrieves data without modifying state.
Register the Storage MCP server in PolicyLayer and add a rule for list_buckets: 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 Storage. Nothing to install.
list_buckets 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_buckets 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_buckets. 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_buckets is provided by the Storage MCP server (@google-cloud/storage-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.