AI agents call watch_status to retrieve information from ScreenHand without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves metadata about registered watch rules and their execution counts. It performs a read-only query with no side effects—no data is created, modified, deleted, or executed. The action is purely informational, making it a Read category tool with low severity since an AI agent cannot cause harm by merely inspecting watch rule status.
From the tool's definition Tool name 'watch_status' and description 'Get all registered watch rules and their fire counts' indicate a query operation that retrieves and displays information about existing watch rules without modifying them.
Documented attack patterns abuse exactly the kind of access watch_status 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 watch_status:
{
"version": "1",
"default": "deny",
"tools": {
"watch_status": {}
}
}watch_status is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Get all registered watch rules and their fire counts. It is categorised as a Read tool in the ScreenHand MCP Server, which means it retrieves data without modifying state.
Register the ScreenHand MCP server in PolicyLayer and add a rule for watch_status: 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.
watch_status is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the watch_status 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 watch_status. 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.
watch_status 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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89 ScreenHand tools catalogued and risk-classified — across an index of 43,000+ MCP servers.