llm_model_usage

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

With no annotations, the description carries full burden for behavioral disclosure. It implies read-only analysis ('shows', 'returns') but does not explicitly state that it is non-destructive or any side effects, authorization needs, or rate limits. This is a significant gap for an analysis 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 concise, front-loading the purpose in the first sentence. It uses bullet points for clarity and avoids extraneous text. Every sentence adds value.

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 tool's simplicity (1 parameter, output schema exists), the description covers the core behavior, output content, and parameter meaning adequately. It does not mention data source or potential latency, but is otherwise complete for typical usage.

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 description adds meaning beyond the schema: 'hours: Look back this many hours (default: 24)'. The schema only provides type and default, so this explanation is valuable. Since there is only one parameter and schema coverage is 0%, 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 the tool's purpose: 'Analyze which models are being selected in routing.' It lists specific statistics shown (top models, task type distribution, etc.). However, it does not explicitly differentiate from sibling tools like llm_usage, which may have overlapping functionality.

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?

No guidance is provided on when to use this tool versus alternatives. It does not state prerequisites, scenarios, or exclude cases. The description simply describes what it does without contextual usage advice.

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