apply_safe_state
Emergency action: write the scientist-configured safe-state PVs (--option safe_state, e.g.
This record as markdown: /tools/lab-instrument-mcps/apply-safe-state.md
What apply_safe_state does on Pypi:labmcp Alicat
AI agents use apply_safe_state to create or update resources in Pypi:labmcp Alicat, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Pypi:labmcp Alicat environment.
Why apply_safe_state is rated Medium
Writes predefined safe-state values to instrument PVs; reversible but high-impact emergency action.
From the tool's definition Emergency action: write the scientist-configured safe-state PVs
Attacks that exploit this kind of access
The rule that runs apply_safe_state 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 apply_safe_state, this is the rule to start with:
apply_safe_state stays usable, but capped: an agent stuck in a loop can't make hundreds of changes 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 apply_safe_state call is checked against it from then on.
Questions about apply_safe_state
Emergency action: write the scientist-configured safe-state PVs (--option safe_state, e.g. It is categorised as a Write tool in the Pypi:labmcp Alicat MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Pypi:labmcp Alicat MCP server in PolicyLayer and add a rule for apply_safe_state: 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.
apply_safe_state is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the apply_safe_state 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 apply_safe_state. 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.
apply_safe_state 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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