clear_results

Delete all received messages and results from this server's memory (e.g. between runs).

SERVERPypi:labmcp Alicat SOURCEpypi:labmcp-alicat

Critical RISK CLASS

Category Destructive

Parameters 00 required

Recommended Hiddensee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/lab-instrument-mcps/clear-results.md

WHAT IT DOES

What clear_results does on Pypi:labmcp Alicat

AI agents call clear_results to permanently remove resources in Pypi:labmcp Alicat, typically in cleanup and lifecycle workflows. It does its job in a single call, and there is no undo.

RISK

Why clear_results is rated Critical

Irreversibly removes data without undo; prevents audit trails and experiment records.

From the tool's definition Delete all received messages and results from this server's memory.

Attacks that exploit this kind of access

RECOMMENDED RULE

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

clear_results Hidden

clear_results is removed from the agent's tool list entirely, so the agent never calls it. The rest of the server keeps working.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "hide": [
    "clear_results"
  ]
}

RESTRICT 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 clear_results call is checked against it from then on.

FAQ

Questions about clear_results

What does the clear_results tool do? +

Delete all received messages and results from this server's memory (e.g. between runs). It is categorised as a Destructive tool in the Pypi:labmcp Alicat MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.

How do I enforce a policy on clear_results? +

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

clear_results is a Destructive tool with critical risk. Critical-risk tools should be blocked by default and only enabled with explicit human approval.

Can I rate-limit clear_results? +

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

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

clear_results 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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