ml_evaluate_model

Get accuracy, training status, and metrics for a trained ML solution

SERVERServiceNow-MCP SOURCEtedorigawa001/servicenow-mcp

Low RISK CLASS

Category Read

Parameters 00 required

Recommended Allowedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/tedorigawa001-servicenow-mcp/ml-evaluate-model.md

WHAT IT DOES

What ml_evaluate_model does on ServiceNow-MCP

AI agents call ml_evaluate_model to retrieve information from ServiceNow-MCP without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.

RISK

Why ml_evaluate_model is rated Low

The tool performs read-only operations: it fetches accuracy metrics, training status, and performance data from an already trained ML solution. There are no side effects, data modifications, code execution, or irreversible actions. This is a pure information retrieval operation, placing it firmly in the Read category with low severity.

From the tool's definition Tool description states 'Get accuracy, training status, and metrics' — retrieves and queries information about ML model performance without modification or execution of external operations.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs ml_evaluate_model 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_evaluate_model, this is the rule to start with:

ml_evaluate_model Allowed

ml_evaluate_model is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "ml_evaluate_model": {}
  }
}

ALLOW ONLY THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect ServiceNow-MCP, apply this rule, and every ml_evaluate_model call is checked against it from then on.

FAQ

Questions about ml_evaluate_model

What does the ml_evaluate_model tool do? +

Get accuracy, training status, and metrics for a trained ML solution. It is categorised as a Read tool in the ServiceNow-MCP MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on ml_evaluate_model? +

Register the ServiceNow MCP server in PolicyLayer and add a rule for ml_evaluate_model: 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.

What risk level is ml_evaluate_model? +

ml_evaluate_model is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit ml_evaluate_model? +

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

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

ml_evaluate_model 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.

KEEP EXPLORING

More on ServiceNow, and thousands of servers like it.

This server

All 495 ServiceNow tools →

Across the catalogue

emit_invariant_model on 0sec → The MCP Attack Database →

Guides

Roll out MCP under one policy →Data exfiltration →MCP token cost →