llm_gain

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

Without annotations, the description carries the burden. It implies a read-only operation returning a dashboard but doesn't explicitly state no side effects, rate limits, or auth requirements. Adequate for a simple dashboard tool 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 an overview, bullet points for features, and clear Args/Returns sections. It is fairly concise, though the features list could be slightly more compact.

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?

With one simple parameter and an output schema present, the description covers the essential behavior and parameter details. It could mention the return format type but is sufficient given the tool's simplicity.

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 schema coverage is 0%, so the description fully explains the single parameter 'period' with valid values ('today', 'week', 'month', 'all') and default ('week'), adding crucial meaning beyond the schema's type-only definition.

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 'Show token savings dashboard' and lists specific features like total savings, efficiency multiplier, and breakdowns. It distinguishes from siblings like llm_budget by focusing on savings, but doesn't explicitly differentiate from llm_savings, which may overlap.

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 no guidance on when to use this tool versus alternatives like llm_savings or llm_dashboard. It only describes what it does, not the context or criteria for selection.

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