Automatically predicts incident category, assignment group, and priority using short description and optional full description for accurate routing.
Use Predictive Intelligence to predict category, assignment group, and priority (latest release: LightGBM algorithm)
| Name | Required | Description | Default |
|---|---|---|---|
| description | No | Optional full description for better accuracy | |
| short_description | Yes | Incident short description |
Changes observed during successful MCP inspections.
v1.0.2Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, open-world behavior. Description adds algorithm version but no additional behavioral traits like error handling or output format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with purpose, no redundant text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, and description does not describe the prediction result format. For a tool that returns predictions, the output structure is crucial context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema provides full descriptions for both parameters. Description adds 'for better accuracy' but no new semantic detail beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it predicts category, assignment group, and priority using Predictive Intelligence with LightGBM. It distinguishes from sibling tools like ml_train_incident_classifier and get_incident.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies use for prediction on incident data, but does not explicitly state when to use vs alternative prediction tools or exclude scenarios. No when-not guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.