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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, not open-world), and the description adds substantial value on top: the data is 'as recorded by AI Citizenship' and may not reflect current rules, the disclaimer must be shown, multiple test versions may exist and must not be assumed, and no personal information may be collected. That is exactly the kind of caveat and handling context the annotations cannot express.

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 purpose and parameter behavior are front-loaded and efficient, but the six-item numbered rules block is long and largely policy boilerplate that is not specific to invoking this tool. It is not wasted entirely, but it dilutes the actionable 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?

There is no output schema, so the description carries the burden of explaining returns, and it does: it lists the fields of the list mode and the detail mode. Combined with the staleness/disclaimer caveats and version-note handling, an agent has everything needed 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%, so both parameters are already documented (lang for names, country for detail). The description goes beyond that by explaining what the country parameter returns in detail mode (topic names, free and full question counts, notices), which clarifies the list-vs-detail behavioral split not visible in the schema.

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 gives a specific verb and resource ('List the citizenship tests AI Citizenship covers') and then enumerates exactly what the listing contains: country codes, test format, time limits, pass marks, languages, topics and free-question counts. That scope is clearly distinct from check_answer and get_practice_questions. It does not, however, name a sibling tool to route against, so it stops short of a 5.

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 gives one concrete usage cue: 'Pass country for one country's detail (topic names, free and full question counts, notices)', which tells the agent when to use the parameter. Beyond that there is no explicit when-to-use-this-vs-alternatives guidance relative to the sibling practice tools, leaving usage to inference.

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