call_command

A execute tool on the Pypi:labmcp Alicat MCP server.

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/call-command.md

WHAT IT DOES

What call_command does on Pypi:labmcp Alicat

AI agents invoke call_command 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 call_command is rated High

Generic command execution on lab instruments could trigger arbitrary operations with physical consequences.

From the tool's definition uninformative description

Attacks that exploit this kind of access

RECOMMENDED RULE

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

call_command Rate-limited

call_command 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": {
    "call_command": {
      "limits": [
        {
          "counter": "call_command_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 call_command call is checked against it from then on.

FAQ

Questions about call_command

What does the call_command tool do? +

call_command is a execute tool on the Pypi:labmcp Alicat MCP server. 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 call_command? +

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

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

Can I rate-limit call_command? +

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

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

call_command 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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