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

The annotations already declare read-only, idempotent, non-destructive, and closed-world behavior, so the safety profile is covered. The description adds important caveat context beyond annotations: latency is estimated from a great-circle distance fibre-propagation model, not measured.

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 definition is front-loaded with the core optimization objective, followed by one concise caveat sentence. The formula earns its place by specifying the exact weighted-latency criterion, and there is no redundant filler.

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?

An output schema exists, so return-value details need not be described. With full parameter descriptions, safety annotations, and the estimation-model caveat, the description is complete for an agent to call and interpret this tool correctly.

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%, including descriptions for 'hosts', 'users', and 'provider'. The description references population-weighted inputs but does not add syntax, format, or provider-filtering details beyond what the schema already provides, so the baseline of 3 is appropriate.

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 states a specific verb and resource: it finds the cloud host region minimizing population-weighted latency and computes an optimal multi-region split. The title and description make the cloud-host latency scope clear, distinguishing it from the country-data siblings.

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 opening phrase 'Given a user-population breakdown' clearly establishes the input scenario in which this tool applies. It does not name explicit exclusions or alternatives, but no sibling appears to cover the same cloud-latency optimization use case.

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