run_internal_adjustment

Adjust (calibrate) the balance with its built-in reference weight (MT-SICS C3).

SERVERPypi:labmcp Alicat SOURCEpypi:labmcp-alicat

High RISK CLASS

Category Execute

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/run-internal-adjustment.md

WHAT IT DOES

What run_internal_adjustment does on Pypi:labmcp Alicat

AI agents invoke run_internal_adjustment 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.

RISK

Why run_internal_adjustment is rated High

Calibration adjusts instrument state and behavior; effects depend on instrument state and could affect downstream measurements if misconfigured.

From the tool's definition Adjust (calibrate) balance with built-in reference weight (MT-SICS C3)

Attacks that exploit this kind of access

RECOMMENDED RULE

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

run_internal_adjustment Rate-limited

run_internal_adjustment 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.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "run_internal_adjustment": {
      "limits": [
        {
          "counter": "run_internal_adjustment_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

RATE-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 run_internal_adjustment call is checked against it from then on.

FAQ

Questions about run_internal_adjustment

What does the run_internal_adjustment tool do? +

Adjust (calibrate) the balance with its built-in reference weight (MT-SICS C3). 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.

How do I enforce a policy on run_internal_adjustment? +

Register the Pypi:labmcp Alicat MCP server in PolicyLayer and add a rule for run_internal_adjustment: 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 run_internal_adjustment? +

run_internal_adjustment is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.

Can I rate-limit run_internal_adjustment? +

Yes. Add a rate_limit block to the run_internal_adjustment 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 run_internal_adjustment completely? +

Set action: deny in the PolicyLayer policy for run_internal_adjustment. 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 run_internal_adjustment? +

run_internal_adjustment 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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