llm_gain

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

No annotations are provided, so the description must fully disclose behavior. It indicates a read-only dashboard function without side effects, but does not explicitly state that it is non-destructive or mention any authentication needs or rate limits. The description is adequate but not thorough.

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 intro, bullet-pointed features, and explicit Args/Returns sections. It is front-loaded with the core purpose. Minor redundancy (e.g., 'RTK-style' may not be universally understood) 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 presence of an output schema, the description does not need to detail return values but does mention 'Formatted savings dashboard'. It covers the parameter and provides an overview of metrics. For a dashboard tool, it is fairly complete, though an example would enhance completeness.

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 input schema only specifies a default value for 'period', but the description adds the allowed values ('today', 'week', 'month', 'all') and clarifies the default is 'week'. This provides essential semantic meaning beyond the schema, compensating for the 0% schema description coverage.

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 (RTK-style)' and lists specific metrics displayed. It distinguishes itself from sibling tools like llm_dashboard or llm_savings by specifying token savings across routing decisions, making its purpose unmistakable.

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 clear context on when to use this tool (to view token savings metrics) but does not explicitly mention when not to use it or alternatives. However, the purpose is specific enough to infer appropriate usage scenarios.

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