review_change
Approve or reject a queued change. Approving marks it as ready to execute — use apply_change afterward. Rejecting discards it permanently.
This record as markdown: /tools/vamerli-elementify-mcp/review-change.md
What review_change does on Elementify MCP
AI agents call review_change to permanently remove resources in Elementify MCP, typically in cleanup and lifecycle workflows. It does its job in a single call, and there is no undo.
Why review_change is rated Critical
An AI agent that decides to call review_change doesn't hesitate, doesn't double-check, and doesn't stop at one. Whatever it removes from Elementify MCP is gone. There is no undo for destructive operations.
Attacks that exploit this kind of access
The rule that runs review_change safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Elementify MCP, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For review_change, this is the rule to start with:
review_change is removed from the agent's tool list entirely, so the agent never calls it. The rest of the server keeps working.
The button opens the PolicyLayer dashboard: create your workspace, connect Elementify MCP, apply this rule, and every review_change call is checked against it from then on.
Questions about review_change
Approve or reject a queued change. Approving marks it as ready to execute — use apply_change afterward. Rejecting discards it permanently. It is categorised as a Destructive tool in the Elementify MCP MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the Elementify MCP server in PolicyLayer and add a rule for review_change: 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 Elementify MCP. Nothing to install.
review_change is a Destructive tool with critical risk. Critical-risk tools should be blocked by default and only enabled with explicit human approval.
Yes. Add a rate_limit block to the review_change 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 review_change. 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.
review_change is provided by the Elementify MCP server (vamerli/elementify-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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