category_breakdown

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

With no annotations provided, the description carries the burden of behavioral disclosure. It explicitly states 'Read-only,' which is a key behavioral trait. The word 'Aggregate' also implies a non-mutating operation. This covers the most critical behavior for an agent to know before invoking the tool.

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 with a dash and a short 'Read-only' addition. It is front-loaded with the core purpose, and every word earns its place. There is no fluff or redundancy.

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?

The tool has an output schema, so the return format is covered by that. The description explains what the tool does (aggregate counts by the three dimensions) and that it is read-only. It does not mention any filters or time ranges, but with no parameters, that is likely inherent. The description is complete enough for a no-parameter aggregation tool.

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 tool has 0 parameters, so per rubric the baseline is 4. There is nothing to explain about parameters. The description correctly avoids inventing parameter details.

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 the tool's purpose: aggregate ticket counts grouped by category, priority, and status. This is a specific verb (aggregate) and resource (ticket counts) with clear grouping dimensions. It naturally distinguishes from sibling tools like list_open_tickets (which lists individual tickets) by emphasizing aggregation rather than listing.

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 a clear use case: 'useful for seeing what's piling up in the queue.' This gives the agent a sense of when to use it (to get a high-level view of backlog distribution). It does not explicitly mention when not to use it or point to alternatives, but given the distinct aggregation nature, the context is clear enough for an agent to infer appropriate usage.

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