set_averaging

Set how many samples the meter averages for each reading. More averaging lowers noise but

SERVERPypi:labmcp Alicat SOURCEpypi:labmcp-alicat

Medium RISK CLASS

Category Write

Parameters 00 required

Recommended Rate-limitedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/lab-instrument-mcps/set-averaging.md

WHAT IT DOES

What set_averaging does on Pypi:labmcp Alicat

AI agents use set_averaging 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.

RISK

Why set_averaging is rated Medium

Modifies instrument measurement settings reversibly; affects data quality but not destructively or financially.

From the tool's definition Set how many samples the meter averages for each reading. Configuration adjustment.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs set_averaging 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 set_averaging, this is the rule to start with:

set_averaging Rate-limited

set_averaging 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.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "set_averaging": {
      "limits": [
        {
          "counter": "set_averaging_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

LIMIT THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect Pypi:labmcp Alicat, apply this rule, and every set_averaging call is checked against it from then on.

FAQ

Questions about set_averaging

What does the set_averaging tool do? +

Set how many samples the meter averages for each reading. More averaging lowers noise but. 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.

How do I enforce a policy on set_averaging? +

Register the Pypi:labmcp Alicat MCP server in PolicyLayer and add a rule for set_averaging: 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.

What risk level is set_averaging? +

set_averaging is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit set_averaging? +

Yes. Add a rate_limit block to the set_averaging 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.

How do I block set_averaging completely? +

Set action: deny in the PolicyLayer policy for set_averaging. 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.

What MCP server provides set_averaging? +

set_averaging 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.

KEEP EXPLORING

More on Pypi:labmcp Alicat, and thousands of servers like it.

This server

All 242 Pypi:labmcp Alicat tools →

Across the catalogue

emit_assumption_set on 0sec →set-prioity on Kanban MCP Server → The MCP Attack Database →

Guides

Govern customer-ops agents →Argument validation →Scoped token →