Get training run history and accuracy trends for an ML solution over time
Part of the NowAIKit — ServiceNow AI Toolkit server.
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AI agents invoke ml_model_training_history to trigger processes or run actions in NowAIKit — ServiceNow AI Toolkit. 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_model_training_history 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_model_training_history": {
"limits": [
{
"counter": "ml_model_training_history_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}See the full NowAIKit — ServiceNow AI Toolkit policy for all 446 tools.
These attack patterns abuse exactly the kind of access ml_model_training_history 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.
Get training run history and accuracy trends for an ML solution over time. 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_model_training_history: 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_model_training_history 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_model_training_history 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_model_training_history. 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_model_training_history 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.
Deterministic rules across all 446 NowAIKit — ServiceNow AI Toolkit tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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