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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It details the return structure ('geo.countries: each country name maps to country (label) and regions (array of subregion names). Also returns msg.') and mentions a token cost. However, it does not explain what 'msg' contains, and while read-only behavior is implied, it is not stated explicitly. This is a minor gap given the tool's simplicity.

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 compact, with a clear opening sentence stating the purpose, a structured explanation of the return value, and specific usage guidance. Every sentence adds value, including the note about token cost, with no redundant or overly verbose content.

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 zero-parameter lookup tool, the description covers all necessary aspects: what it lists, the exact output format, and how to use it in a broader workflow (with interest-over-time/interest-by-region). The presence of an output schema further backs return type details, so the description is fully sufficient for the AI agent to use the 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?

There are zero parameters, so schema coverage is trivially 100%. The description adds value by explaining what the returned data is used for (e.g., passing to other Google Trends tools) rather than focusing on parameters, which is appropriate for a parameterless tool.

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 tool's function: 'List all countries and subregions you can pass to other Google Trends tools in the country and region fields.' It uses a specific verb (list) and identifies the resource (geographic regions), while distinguishing itself from sibling tools by mentioning the use case for other Google Trends calls and contrasting with google-trends.categories.

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?

It explicitly provides usage context: 'Use this before interest-over-time or interest-by-region calls when filtering by geography. Pair with google-trends.categories when filtering by category.' This tells the agent when to invoke the tool and how it relates to alternatives, satisfying the criterion for clear when/when-not/alternative guidance.

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