llm_gain

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

No annotations provided. The description implies read-only behavior (dashboard) but does not explicitly state safety, permissions, or side effects. Leaves gaps for a tool with no annotation coverage.

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?

Description is concise with bullet points and front-loaded purpose. Minor redundancy between first two sentences but overall efficient.

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 (one parameter) and the presence of an output schema, the description adequately covers the dashboard's metrics and parameter options. Return value is vague but acceptable with output schema.

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 Args section adds meaning beyond the input schema by listing allowed values ('today', 'week', 'month', 'all') and default. Schema coverage was 0%, so 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 shows a token savings dashboard with specific metrics (efficiency multiplier, breakdowns). However, it does not differentiate from sibling tools like llm_savings or llm_dashboard.

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 on when to use this tool versus alternatives. No explicit when-not or context for selection among siblings.

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