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

Annotations already cover read-only, idempotent, open-world, and non-destructive behavior. The description adds useful context beyond those: the 'provisional' nature of the data, the NCHS dataset identifier, and the ISO-week granularity. This meaningfully informs the agent about data freshness and structure without contradicting 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?

Two sentences deliver the dataset source, output semantics, granularity, and intended use cases with no filler. Key information is front-loaded and every 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?

For a simple read-only tool with all parameters documented in the schema and no required parameters, the description is complete enough for safe invocation. It explains what the tool returns, from which dataset, and what analysis it supports, which compensates for the absence of an output schema.

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 baseline is 3. The description hints at cause categories and state/time granularity, but it does not add much beyond what the schema already documents for year, cause, limit, and state.

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 states a specific verb and resource: 'Returns all-cause and selected-cause death counts per state per ISO week' from the CDC NCHS dataset. It identifies the exact dataset id (muzy-jte6) and scope, which clearly distinguishes it from sibling CDC tools like cdc_flu_surveillance or cdc_leading_causes_of_death.

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 gives clear context for appropriate use: 'excess-mortality and respiratory-disease seasonality analysis.' It does not explicitly name sibling tools to avoid or state when-not-to-use conditions, so it stops short of a 5.

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