all_heaters_off
Turn every output off (heater range 0 on outputs 1-4), like the front-panel All Off key,
This record as markdown: /tools/lab-instrument-mcps/all-heaters-off.md
What all_heaters_off does on Pypi:labmcp Alicat
AI agents invoke all_heaters_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_heaters_off is rated High
Simultaneously disables all heaters, potentially disrupting ongoing experiments or samples.
From the tool's definition Turn every output off, heater range 0 on outputs 1-4
Risk signalsBulk/mass operation — affects multiple targets
Attacks that exploit this kind of access
The rule that runs all_heaters_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_heaters_off, this is the rule to start with:
all_heaters_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_heaters_off call is checked against it from then on.
Questions about all_heaters_off
Turn every output off (heater range 0 on outputs 1-4), like the front-panel All Off key,. 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_heaters_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_heaters_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_heaters_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_heaters_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_heaters_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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