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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, openWorld). The description adds meaningful context beyond them: the data is 'provisional' (subject to revision) and reported at ISO-week granularity, plus what the response contains. It stops short of noting pagination/row-limit behavior.

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 tight sentences, front-loaded with the resource and source dataset, then return shape, then use case. No filler or repetition; each sentence earns its place.

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 no output schema, the description correctly summarizes the return values and notes provisional status. Given rich annotations and full schema coverage, it is nearly complete; the main gap is lack of differentiation from the crowded set of CDC death/cause siblings.

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 coverage is 100%, so year, cause, limit, and state are all documented in the schema itself. The description adds only that counts are per state and per ISO week, which is marginal value over the structured fields. Baseline 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?

Names a specific resource ('CDC weekly provisional deaths by state and cause'), identifies the source dataset (NCHS muzy-jte6), and states the return shape. It is clear, but does not differentiate itself from close siblings like cdc_excess_deaths_covid or cdc_leading_causes_of_death, which an agent would need help distinguishing.

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 offers a use case ('excess-mortality and respiratory-disease seasonality analysis'), which implies when the tool is relevant. However, it gives no explicit when-not-to-use guidance and does not route the agent to any of the many overlapping CDC siblings, leaving selection largely to inference.

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