categorize_incident

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate 'readOnlyHint: true' and 'openWorldHint: true', which already convey that the tool is read-only and may produce variable results. The description adds the detail of using 'Predictive Intelligence' with the 'LightGBM algorithm', which provides some insight into the underlying mechanism. However, it does not discuss what happens if the prediction fails, confidence levels, or any side effects.

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?

The description is a single, concise sentence of 15 words. It directly states the purpose without any fluff. The key information is front-loaded: 'Use Predictive Intelligence to predict...' The mention of the algorithm version is minor but acceptable.

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?

Given the complexity of the tool (predicting three fields) and the absence of an output schema, the description should provide clarity on what the tool returns (e.g., predicted values with confidence scores). It lacks this information. Additionally, with sibling tools like 'ml_auto_categorize', the description does not differentiate usage, leaving the agent with insufficient context to choose correctly.

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?

The input schema has 100% coverage with descriptions for both parameters ('short_description' and 'description'). The description does not add additional parameter-level information beyond what the schema provides. Therefore, a baseline score of 3 is appropriate.

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 states it 'Use Predictive Intelligence to predict category, assignment group, and priority', which clearly indicates the tool's function: to predict three specific fields. The verb 'predict' is appropriate, and the resource is implied by the tool name 'categorize_incident'. However, it does not explicitly state that the input is an incident description, relying on the name for clarity.

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?

The description provides no guidance on when to use this tool versus alternatives like 'ml_auto_categorize' or 'ml_similar_incidents'. There is no mention of prerequisites, typical use cases, or when not to use it. The agent would have to infer usage from the name and limited context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.