categorize_incident

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

Annotations already declare read-only/open-world hints; the description adds non-obvious context about the Table API mechanism and the PI endpoint limitation. It does not contradict annotations and reveals that the result is a suggestion, not a persistent update.

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?

Two focused sentences; the first states the purpose, the second adds an important limitation/alternative. No redundant filler.

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?

For a two-parameter read-only suggestion tool with annotations, the description provides sufficient context including mechanism, output fields, and an important PI limitation. It could slightly improve by naming the exact sibling alternative or return structure, but current coverage is strong.

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% parameter coverage, so the baseline is 3. The main description does not add parameter-specific details beyond what the schema already provides; it only reinforces that short_description drives the heuristic.

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 opens with a specific verb ('Suggest') and clearly identifies the resource (incident) and outputs (category, assignment group, priority). The phrase 'by analysing similar resolved incidents (Table API)' distinguishes it from sibling ML/PI-based tools by naming the mechanism.

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?

It gives clear context that this is a Table API heuristic and explicitly states that PI has no public REST prediction endpoint, directing model-based needs to PI on-record. It does not name sibling tools like ml_auto_categorize, but the boundary is sufficiently clear.

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