ml_train_incident_classifier
Trigger training of the incident classification ML solution. [Write]
This record as markdown: /tools/nowaikit-servicenow-ai-toolkit/ml-train-incident-classifier.md
What ml_train_incident_classifier does on NowAIKit — ServiceNow AI Toolkit
AI agents invoke ml_train_incident_classifier to trigger actions in NowAIKit — ServiceNow AI Toolkit. 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.
Why ml_train_incident_classifier is rated High
Although the tool includes a [Write] hint, triggering ML model training is fundamentally an Execute action: it runs an external operation (ML training pipeline) whose effects depend on arguments and state. Training can be resource-intensive, may affect downstream systems relying on the model, and produces non-trivial side effects.
From the tool's definition Trigger training of the incident classification ML solution — the verb 'trigger' indicates an active operation that initiates a machine learning training job, not a simple data modification.
Attacks that exploit this kind of access
The rule that runs ml_train_incident_classifier safely
PolicyLayer is an MCP gateway: it sits between your AI agents and NowAIKit — ServiceNow AI Toolkit, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For ml_train_incident_classifier, this is the rule to start with:
ml_train_incident_classifier 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.
The button opens the PolicyLayer dashboard: create your workspace, connect NowAIKit — ServiceNow AI Toolkit, apply this rule, and every ml_train_incident_classifier call is checked against it from then on.
Questions about ml_train_incident_classifier
Trigger training of the incident classification ML solution. [Write]. It is categorised as a Execute tool in the NowAIKit — ServiceNow AI Toolkit MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the NowAIKit — ServiceNow AI Toolkit MCP server in PolicyLayer and add a rule for ml_train_incident_classifier: 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 NowAIKit — ServiceNow AI Toolkit. Nothing to install.
ml_train_incident_classifier is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the ml_train_incident_classifier 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.
Set action: deny in the PolicyLayer policy for ml_train_incident_classifier. 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.
ml_train_incident_classifier is provided by the NowAIKit — ServiceNow AI Toolkit MCP server (nowaikit). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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