operator_work_prune

Run operator-work.prune through the Appaloft application operation catalog. Shared with CLI and HTTP/API.

SERVERAppaloft SOURCE@appaloft/mcp

High RISK CLASS

Category Execute

Parameters 31 required

Recommended Rate-limitedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/appaloft-mcp/operator-work-prune.md

WHAT IT DOES

What operator_work_prune does on Appaloft

AI agents invoke operator_work_prune to trigger actions in Appaloft. 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.

Parameter	Type	Required
`before`	string	Yes
`dryRun`	boolean	—
`statuses`	array	—

Parameters from the server's own tool schema.

RISK

Why operator_work_prune is rated High

operator_work_prune triggers real processes with real consequences. An agent gone sideways doesn't fire it once. It starts dozens of builds, sends mass notifications, or burns through compute before anyone looks up.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs operator_work_prune safely

PolicyLayer is an MCP gateway: it sits between your AI agents and Appaloft, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For operator_work_prune, this is the rule to start with:

operator_work_prune Rate-limited

operator_work_prune 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": {
    "operator_work_prune": {
      "limits": [
        {
          "counter": "operator_work_prune_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 Appaloft, apply this rule, and every operator_work_prune call is checked against it from then on.

FAQ

Questions about operator_work_prune

What does the operator_work_prune tool do? +

Run operator-work.prune through the Appaloft application operation catalog. Shared with CLI and HTTP/API. It is categorised as a Execute tool in the Appaloft MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

What parameters does operator_work_prune accept? +

operator_work_prune accepts 3 parameters: before, dryRun, statuses. Required: before. The full parameter table on this page comes from the server's own tool schema.

How do I enforce a policy on operator_work_prune? +

Register the Appaloft MCP server in PolicyLayer and add a rule for operator_work_prune: 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 Appaloft. Nothing to install.

What risk level is operator_work_prune? +

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

Can I rate-limit operator_work_prune? +

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

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

operator_work_prune is provided by the Appaloft MCP server (@appaloft/mcp). 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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