ml_train_anomaly_detector
Trigger training of an anomaly detection model for a specific table/field. [Write]
This record as markdown: /tools/tedorigawa001-servicenow-mcp/ml-train-anomaly-detector.md
What ml_train_anomaly_detector does on ServiceNow-MCP
AI agents invoke ml_train_anomaly_detector to trigger actions in ServiceNow-MCP. 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_anomaly_detector is rated High
Although labeled [Write], the tool's core function is to execute (trigger/initiate) an ML training process, not merely create or modify reversible data records. ML training is computational execution with non-trivial side effects and resource consumption. This falls under Execute rather than Write.
From the tool's definition Trigger training of an anomaly detection model — this invokes a machine learning pipeline execution with side effects that depend on the specified table/field arguments.
Attacks that exploit this kind of access
The rule that runs ml_train_anomaly_detector safely
PolicyLayer is an MCP gateway: it sits between your AI agents and ServiceNow-MCP, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For ml_train_anomaly_detector, this is the rule to start with:
ml_train_anomaly_detector 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 ServiceNow-MCP, apply this rule, and every ml_train_anomaly_detector call is checked against it from then on.
Questions about ml_train_anomaly_detector
Trigger training of an anomaly detection model for a specific table/field. [Write]. It is categorised as a Execute tool in the ServiceNow-MCP MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the ServiceNow MCP server in PolicyLayer and add a rule for ml_train_anomaly_detector: 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 ServiceNow-MCP. Nothing to install.
ml_train_anomaly_detector 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_anomaly_detector 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_anomaly_detector. 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_anomaly_detector is provided by the ServiceNow MCP server (tedorigawa001/servicenow-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
More on ServiceNow, and thousands of servers like it.
Across the catalogue