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

Annotations already establish that the tool is read-only, idempotent, and non-destructive, so the safety burden is covered. The description adds useful context about the data being modeled expected vs observed values, but it does not disclose operational details such as update cadence, response shape, or query limitations. This is adequate but not rich behavioral disclosure.

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 short sentences each earn their place: the dataset identifier, the data model and granularity, and the intended analytical purpose. There is no filler, repetition of schema content, or unnecessary detail. The key information is front-loaded.

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 parameters and safety annotations, the description is sufficient for an agent to understand what the tool returns and why it exists. It does not describe output columns or response formatting, but no output schema is provided and the dataset semantics are clear enough for correct invocation.

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 the schema already documents limit, state, and outcome with examples. The description adds the conceptual framing of 'by state and week' but gives no parameter-specific guidance beyond that. The baseline of 3 is appropriate because the schema carries the parameter documentation burden.

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 identifies a specific CDC dataset (xkkf-xrst) and explains that it contains modeled expected vs observed deaths by state and week. This distinguishes it from siblings like cdc_weekly_deaths_by_state, which would provide observed death data, and cdc_drug_overdose_deaths. Though no explicit verb like 'get' or 'query' appears, the resource and its content are unmistakable.

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 states a clear purpose: estimating true pandemic impact beyond reported COVID deaths. This gives an agent enough context to select this tool over related CDC mortality datasets. It does not explicitly name alternative tools or state when not to use it, but the intended use case is concrete rather than merely implied.

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