Trigger training of an anomaly detection model for a specific table/field. [Write]
Part of the ServiceNow MCP Server server.
Free to start. No card required.
AI agents invoke ml_train_anomaly_detector to trigger processes or run actions in ServiceNow MCP Server. Execute operations can have side effects beyond the immediate call -- triggering builds, sending notifications, or starting workflows. Rate limits and argument validation are essential to prevent runaway execution.
ml_train_anomaly_detector can trigger processes with real-world consequences. An uncontrolled agent might start dozens of builds, send mass notifications, or kick off expensive compute jobs. PolicyLayer enforces rate limits and validates arguments to keep execution within safe bounds.
Execute tools trigger processes. Rate-limit and validate arguments to prevent unintended side effects.
{
"version": "1",
"default": "deny",
"tools": {
"ml_train_anomaly_detector": {
"limits": [
{
"counter": "ml_train_anomaly_detector_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}See the full ServiceNow MCP Server policy for all 384 tools.
These attack patterns abuse exactly the kind of access ml_train_anomaly_detector gives an agent. Each links to the full case and the policy that stops it:
Other execute tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Trigger training of an anomaly detection model for a specific table/field. [Write]. It is categorised as a Execute tool in the ServiceNow MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the ServiceNow MCP Server 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 Server. 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 MCP server (@aartiq/servicenow-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 384 ServiceNow MCP Server tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
4,600+ MCP servers and 31,000+ tools scanned and risk-classified.