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

Annotations already establish read-only, non-destructive, idempotent behavior. The description adds valuable context beyond annotations by disclosing that the tool exposes decision logic, claim boundaries, and known biases. This meaningful characterization helps the agent know what kind of information to expect.

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?

Three short sentences, with the most important usage guidance front-loaded and no filler. Each sentence earns its place, and the final instruction reinforces a key scenario without being redundant.

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?

The description fully equips an agent to decide when to invoke this tool and what it will provide. With zero parameters and an output schema available, no additional invocation details are necessary, and the guidance is complete for its role among the listed siblings.

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 tool has zero parameters, and the schema is fully covered with an empty properties object. No parameter explanation is needed; the baseline of 4 applies.

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 what the tool provides: an explanation of how the engine makes decisions, what the published numbers claim or do not claim, and known biases. It is distinct from the sibling tools by positioning itself as the methodology/context provider rather than a data or signal retrieval tool.

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 gives explicit when-to-use guidance: before interpreting any performance or signal claim, especially when assumptions, limitations, or bias are in question. The closing instruction to read it before quoting any statistic from other tools clearly routes agents to this tool ahead of alternatives.

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