ml_auto_categorize

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

Annotations already indicate read-only and open-world hints. The description adds value by specifying it queries the last 500 resolved records and matches keywords, providing concrete behavioral context beyond the annotations. It does not contradict annotations.

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 sentence that conveys the purpose and method without unnecessary words. It is well-structured and front-loaded with the main action. Could be slightly improved with bullet points or structured formatting.

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 simple tool with no output schema, the description covers the core functionality: analyzing records and suggesting a category. It does not discuss edge cases or return format, but that is acceptable given the tool's simplicity and high schema coverage.

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 coverage is 100%, so the schema already documents all parameters. The description mentions 'short_description' for keyword matching and 'description' optional for accuracy, but this does not add significant meaning beyond the schema definitions.

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 uses a specific verb ('auto-categorize') and resource ('record'), and clearly explains the method: analyzing resolved records of the same table and matching keywords. This distinguishes it from siblings like 'categorize_incident' which may not use historical analysis.

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 implies usage for automatic categorization based on historical data but does not explicitly state when to use this tool over alternatives like 'categorize_incident' or 'ml_similar_incidents'. No 'use when' or 'when not to use' guidance is provided.

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