CDC Mortality (WONDER)

get_mortality_by_state

Compare mortality rates across states for a specific cause or all causes.

Returns age-adjusted death rates by state, useful for comparing mortality
burdens across geographies. Data from NCHS Leading Causes of Death.

Args:
    cause: Cause of death to filter by (e.g. 'Heart disease', 'Cancer',
        'Unintentional injuries', 'Alzheimer\'s disease'). Partial match
        supported. Returns all causes if not specified.
    year: Filter by year (e.g. 2017). Defaults to most recent available year
        if not specified.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`year`	No
`cause`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.6/5.0

Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses key behaviors: returns age-adjusted death rates, supports partial matching for causes, and defaults to all causes and the most recent year. This provides meaningful context beyond the schema, though it does not mention pagination or response format (output schema exists).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured, with a clear opening statement, a sentence on return value and data source, followed by an Args section. Every sentence adds value, and the actionable details are front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is complete for a data-retrieval tool with two optional parameters and an output schema. It covers the tool's purpose, output type, data source, parameter semantics, and defaults. Given the output schema exists, the lack of explicit return value details is not a gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description fully compensates. For 'cause', it provides examples, notes partial match support, and clarifies the default (returns all causes). For 'year', it gives an example and states the default (most recent year). This adds significant meaning beyond the raw schema fields.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool compares mortality rates across states for a specific cause or all causes, with a specific verb ('Compare') and resource ('mortality rates across states'). It also specifies the output ('age-adjusted death rates by state') and data source, distinguishing it from sibling mortality tools like drug overdose, infant mortality, and leading causes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states the tool is 'useful for comparing mortality burdens across geographies', providing clear context for when to use it. It does not explicitly name alternative tools or exclusions, but the use case is well-defined relative to the sibling tools.

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

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TDQS

A4.5/5.0

Disambiguation4/5

The tools are mostly distinct: drug overdose, infant mortality, leading causes, state comparisons, and provisional mortality are clearly different datasets. However, get_leading_causes_of_death and get_mortality_by_state both draw from the same NCHS Leading Causes dataset and could be confused without careful reading.

Naming Consistency5/5

All tools follow a consistent get_<domain>_<focus> pattern in snake_case, making names predictable and easy to remember. There are no mixed conventions or vague verbs.

Tool Count5/5

With 5 tools, the server is well-scoped for the stated purpose of accessing CDC mortality data. The number allows each tool to cover a meaningful dataset without redundancy.

Completeness4/5

The server covers major mortality topics: drug overdoses, infant mortality, leading causes, state comparisons, and provisional data including COVID. Minor gaps exist such as lack of age-specific mortality filters or broader demographic breakdowns, but the core surface is reasonably complete for typical queries.

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