AI agents invoke window_resize to trigger actions in It2mcp. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.
Resizing a window triggers an external operation on the iTerm2 application via the iTerm2 Python API. It modifies the state of a running application (window dimensions), which is an external side-effecting action beyond simple data read/write. While not destructive or financial, it falls under Execute as it triggers an operation in an external system whose effects depend on the arguments provided.
From the tool's definition Resize an iTerm2 window
Attacks that exploit this kind of access
Resize an iTerm2 window. It is categorised as a Execute tool in the It2mcp MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the It2 MCP server in PolicyLayer and add a rule for window_resize: 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 It2mcp. Nothing to install.
window_resize is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the window_resize 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 window_resize. 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.
window_resize is provided by the It2 MCP server (urjitbhatia/it2mcp). 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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