trigger_manual_retraining

Manually trigger ML model retraining (bypasses schedule)

SERVERMcp Windows SOURCEmukul975/mcp-windows-automation

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/mukul975-mcp-windows-automation/trigger-manual-retraining.md

WHAT IT DOES

What trigger_manual_retraining does on Mcp Windows

AI agents invoke trigger_manual_retraining to trigger actions in Mcp Windows. 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 trigger_manual_retraining is rated High

This tool executes a computational process (ML model retraining) that can consume significant system resources, modify model files, and potentially impact dependent systems relying on the model. While not immediately destructive or creating new data in a user-facing sense, it actively triggers and controls a system operation with side effects.

From the tool's definition Tool description states 'Manually trigger ML model retraining (bypasses schedule)' - this action runs/triggers an external operation (model retraining) whose effects depend on system state and training configuration.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs trigger_manual_retraining safely

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

trigger_manual_retraining Rate-limited

trigger_manual_retraining 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": {
    "trigger_manual_retraining": {
      "limits": [
        {
          "counter": "trigger_manual_retraining_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 Mcp Windows, apply this rule, and every trigger_manual_retraining call is checked against it from then on.

FAQ

Questions about trigger_manual_retraining

What does the trigger_manual_retraining tool do? +

Manually trigger ML model retraining (bypasses schedule). It is categorised as a Execute tool in the Mcp Windows MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on trigger_manual_retraining? +

Register the Mcp Windows MCP server in PolicyLayer and add a rule for trigger_manual_retraining: 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 Mcp Windows. Nothing to install.

What risk level is trigger_manual_retraining? +

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

Can I rate-limit trigger_manual_retraining? +

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

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

trigger_manual_retraining is provided by the Mcp Windows MCP server (mukul975/mcp-windows-automation). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

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