AI agents use log_wash to create or update resources in Wardrowbe — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Wardrowbe environment.
This tool modifies the state of a wardrobe item by resetting its wash counter. It is a reversible data update (the counter can be reset again or incremented), so it falls under Write. The blast radius is low — misuse only affects wear/wash tracking data for clothing items.
From the tool's definition Mark `item_id` as washed. Resets wears_since_wash on the backend.
Attacks that exploit this kind of access
Mark item_id as washed. Resets wears_since_wash on the backend. It is categorised as a Write tool in the Wardrowbe MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Wardrowbe MCP server in PolicyLayer and add a rule for log_wash: 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 Wardrowbe. Nothing to install.
log_wash is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the log_wash 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 log_wash. 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.
log_wash is provided by the Wardrowbe MCP server (saya6k/mcp-wardrowbe). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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