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

The annotations already establish that this is a read-only, idempotent, non-destructive tool. The description adds meaningful behavioral context by explaining the data source size, shrinkage toward the average, ranking by the low end of the interval, and precise meanings of the 'proven' and 'promising' tiers.

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 two dense but efficient sentences, front-loading the resource and then adding methodology and tier definitions. Every sentence contributes directly to understanding the output and confidence labels.

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?

For a read-only analytics tool with a fully described schema and safety annotations, the definition provides enough context to call it correctly. It could be stronger by clarifying how this tool differs from related siblings, but the core return concept and tier semantics are covered.

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?

Schema description coverage is 100%, so the baseline is 3. The description goes beyond the schema by defining the confidence tiers 'proven' and 'promising', which adds real semantic meaning to the tier parameter beyond its enum values.

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 resource: reply rates by country × buyer type from real export emails, ranked by the lower bound of a 90% interval. It is specific about the data and methodology, but it does not explicitly differentiate itself from potentially related siblings such as reply_pace or outreach_playbook.

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 intended use is implied by the title and description: identifying country × buyer type segments with high reply likelihood. However, there is no explicit when-to-use guidance, no exclusions, and no reference to alternative sibling tools that might be more appropriate for adjacent questions.

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