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

Annotations already establish read-only, idempotent, non-destructive behavior, so the bar is lower. The description adds useful context: data provenance, measure semantics (notifications vs incidence), and the fields the response will break down. It stops short of describing return formatting or pagination, but that is not critical given the annotations.

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, front-loaded with the data source and key dimensions, with the crucial definitional caveat in the middle and a crisp usage directive at the end. No filler or repetition.

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?

With rich annotations, full schema coverage, and a description that names data source, dimensions, categories, and caveat, an agent has everything needed to select and invoke the tool correctly. The absence of an output schema is mitigated by the enumerated fields in the description. No material gap remains.

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 descriptions cover 100% of parameters, so the baseline is 3. The description reinforces the country/year dimensions and the measure caveat, but does not add parameter-format details beyond what the schema already provides. No deduction is warranted, but there is no extra value either.

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 exact resource (TB notifications reported to WHO by national TB programmes), the dimensions (country, year), and the specific case categories (new/relapse, lab-confirmed vs clinically diagnosed, HIV testing coverage). It also clearly distinguishes the dataset from estimated incidence, so an agent can tell it apart from sibling tools like who_tb_estimates.

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 states a specific use case ('Use for questions about diagnosed and reported TB cases') and an explicit exclusion ('a different measure from estimated incidence'). This tells the agent when to choose this tool rather than an incidence-oriented alternative, though it does not name the sibling explicitly.

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