llm_gain

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

No annotations are provided, so the description must convey behavior. It implies read-only dashboard display but does not explicitly state it's non-destructive, no authentication, or rate limits. The description is adequate but could be more explicit.

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 brief introduction, feature bullet points, and clear args/returns. It is concise but could be slightly more terse. The main idea is front-loaded.

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 (single parameter, no required params), the description covers purpose, usage, parameter details, and return format. The existence of an output schema complements the description.

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?

With 0% schema description coverage, the description compensates by listing allowed values for 'period' (today, week, month, all) and noting the default. This adds meaningful value beyond the schema's type and default.

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 shows a token savings dashboard (RTK-style) and lists specific metrics. It distinguishes itself from sibling tools like llm_savings or llm_budget by emphasizing its comprehensive dashboard nature.

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 the tool's purpose and parameter options (period: today, week, month, all). It doesn't explicitly state when not to use or provide alternatives, but the context of sibling tools serves as implicit guidance.

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