cache_quit
Close the connection to the cache server. Returns: Success message or error message
This record as markdown: /tools/aws/cache-quit.md
What cache_quit does on AWS
AI agents call cache_quit as a supporting operation in AWS workflows.
Why cache_quit is rated Low
This tool closes a connection to a cache server, which is a session/connection management operation. It does not read data, write/modify data, execute code, delete data, or involve financial transactions. Closing a connection is a reversible, administrative action with minimal blast radius.
From the tool's definition Close the connection to the cache server
Attacks that exploit this kind of access
The rule that runs cache_quit safely
PolicyLayer is an MCP gateway: it sits between your AI agents and AWS, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For cache_quit, this is the rule to start with:
cache_quit gets a rate cap, and everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect AWS, apply this rule, and every cache_quit call is checked against it from then on.
Questions about cache_quit
Close the connection to the cache server. Returns: Success message or error message. It is categorised as a Other tool in the AWS MCP Server, which means it performs auxiliary operations.
Register the AWS MCP server in PolicyLayer and add a rule for cache_quit: 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 AWS. Nothing to install.
cache_quit is a Other tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the cache_quit 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 cache_quit. 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.
cache_quit is provided by the AWS MCP server (@awslabs/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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