dispense_ul

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/dispense-ul.md

WHAT IT DOES

What dispense_ul does on Pypi:labmcp Alicat

AI agents invoke dispense_ul 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 dispense_ul is rated High

Dispensing liquids in lab instruments is an irreversible physical action with potential safety implications.

From the tool's definition uninformative description

Attacks that exploit this kind of access

RECOMMENDED RULE

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

dispense_ul Rate-limited

dispense_ul 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": {
    "dispense_ul": {
      "limits": [
        {
          "counter": "dispense_ul_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 dispense_ul call is checked against it from then on.

FAQ

Questions about dispense_ul

What does the dispense_ul tool do? +

dispense_ul 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 dispense_ul? +

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

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

Can I rate-limit dispense_ul? +

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

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

dispense_ul 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

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