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

Annotations already declare readOnlyHint=true. The description adds useful behavioral details beyond annotations: the result is scoped 'across all request types,' rows are sorted by occurrence count, and 'region may be null when unavailable,' which clarifies expected output shape.

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 sentences, each carrying distinct information: result shape, data semantics, and intended use. There is no redundant filler or repetition of schema details.

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 low-complexity, read-only tool with fully documented optional parameters and an output schema present, the description covers purpose, sorting, null behavior, and scope. Nothing essential is missing for an agent to select and invoke it 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%; both the limit and time_filter parameters are fully documented with defaults, formats, and meaning. The description does not add parameter-specific semantics but does not need to, so the baseline score of 3 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 names the resource precisely ('Top (country, region) combinations'), the ordering criterion ('ranked by occurrence count'), and the purpose ('understand geographic distribution of users'). This clearly differentiates it from sibling analytics tools like get_top_languages or get_top_devices.

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 ('Use this to understand geographic distribution of users') and clarifies that results span 'all request types.' However, it does not explicitly mention when not to use it or name an alternative tool for related dimensions, so it stops short of a full usage guide.

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