AI agents invoke observer_stop to trigger actions in ScreenHand. 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.
Stopping a daemon is an Execute action—it triggers an external operation (process termination) whose effects depend on system state. While not destructive (the daemon can be restarted) or directly harmful, it disrupts the server's core functionality and could leave the system in an inconsistent state.
From the tool's definition Tool stops the observer daemon, which is a background process managing accessibility API interactions and browser control. The name 'observer_stop' and description explicitly indicate termination of a running service.
Documented attack patterns abuse exactly the kind of access observer_stop gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and ScreenHand, and nothing reaches the server without passing your rules. This is the rule we recommend for observer_stop:
{
"version": "1",
"default": "deny",
"tools": {
"observer_stop": {
"limits": [
{
"counter": "observer_stop_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}observer_stop stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
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Stop the observer daemon. It is categorised as a Execute tool in the ScreenHand MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the ScreenHand MCP server in PolicyLayer and add a rule for observer_stop: 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 ScreenHand. Nothing to install.
observer_stop 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 observer_stop 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 observer_stop. 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.
observer_stop is provided by the ScreenHand MCP server (manushi4/screenhand). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from ScreenHand, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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