put_pv
A write tool on the Pypi:labmcp Alicat MCP server.
This record as markdown: /tools/lab-instrument-mcps/put-pv.md
What put_pv does on Pypi:labmcp Alicat
AI agents use put_pv 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 put_pv is rated Medium
'put_pv' likely writes a Process Variable to lab instrument control system.
From the tool's definition uninformative description
Attacks that exploit this kind of access
The rule that runs put_pv 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 put_pv, this is the rule to start with:
put_pv 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 put_pv call is checked against it from then on.
Questions about put_pv
put_pv is a write tool on the Pypi:labmcp Alicat MCP server. 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 put_pv: 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.
put_pv 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 put_pv 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 put_pv. 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.
put_pv 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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