llm_model_usage

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

No annotations are provided, so the description carries the burden. It implies a read-only analytical operation but does not explicitly state lack of side effects, access requirements, or data freshness. Returns formatted statistics, but behavioral details are minimal.

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 well-structured with a clear heading, a bulleted list of displayed information, and an Args section. It is concise without being overly brief, earning its sentences.

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 tool with one parameter and an output schema, the description sufficiently explains the return value and parameter. It lacks explicit limitations or performance notes, but given the low complexity, it is mostly complete.

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 only parameter, 'hours,' is explained with its default and purpose in the Args section, adding value beyond the schema (which has title and default but no description). Schema description coverage is 0%, so the description compensates well.

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 'Analyze which models are being selected in routing' and enumerates specific statistics, providing a specific verb-resource pair and distinguishing it from sibling tools like llm_usage and llm_check_usage.

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 explains what the tool shows but does not explicitly state when to use it over alternatives (e.g., llm_usage or llm_check_usage). Usage context is implied but not fully elaborated.

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