all_outputs_off
Switch every output OFF (emergency stop for the whole supply). Each channel is switched
This record as markdown: /tools/lab-instrument-mcps/all-outputs-off.md
What all_outputs_off does on Pypi:labmcp Alicat
AI agents invoke all_outputs_off to trigger actions in Pypi:labmcp Alicat. 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.
Why all_outputs_off is rated High
Triggers an emergency stop across all channels, a broad irreversible action with high blast radius.
From the tool's definition Switch every output OFF (emergency stop for the whole supply)
Risk signalsBulk/mass operation — affects multiple targets
Attacks that exploit this kind of access
The rule that runs all_outputs_off safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Pypi:labmcp Alicat, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For all_outputs_off, this is the rule to start with:
all_outputs_off 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.
The button opens the PolicyLayer dashboard: create your workspace, connect Pypi:labmcp Alicat, apply this rule, and every all_outputs_off call is checked against it from then on.
Questions about all_outputs_off
Switch every output OFF (emergency stop for the whole supply). Each channel is switched. It is categorised as a Execute tool in the Pypi:labmcp Alicat MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Pypi:labmcp Alicat MCP server in PolicyLayer and add a rule for all_outputs_off: 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 Pypi:labmcp Alicat. Nothing to install.
all_outputs_off 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 all_outputs_off 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 all_outputs_off. 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.
all_outputs_off is provided by the Pypi:labmcp Alicat MCP server (pypi:labmcp-alicat). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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