CDC Mortality (WONDER)

get_leading_causes_of_death

Get leading causes of death with death counts and age-adjusted rates.

Returns data from the NCHS Leading Causes of Death dataset, which provides
national and state-level mortality statistics by cause of death, year, and
age-adjusted death rate per 100,000 population. Data spans 1999-2017.

Args:
    state: Filter by state name (e.g. 'California', 'Texas'). Case-insensitive.
        Returns all states if not specified.
    year: Filter by year (e.g. 2017). Returns all available years if not specified.
    limit: Maximum number of records to return (default 25, max 1000).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`year`	No
`limit`	No
`state`	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?

With no annotations provided, the description carries the full transparency burden. It discloses the data source, time range, geographic coverage, case-insensitive state matching, and the limit default/max. It does not discuss ordering or result format, but the presence of an output schema reduces that burden.

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 well-structured with a one-sentence summary followed by a compact Args block. Every line provides useful behavioral or default-value information that is absent from the schema, with no redundant or filler content.

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?

For a read-only query tool with three optional filters and an output schema, the description covers the dataset provenance, temporal span, geographic scope, and parameter semantics. It provides all necessary context for an agent to select and correctly invoke the tool.

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?

The schema provides no descriptions (0% coverage), but the description's Args section fully documents all three parameters: state with examples and case-insensitivity, year with an example, and limit with default/max. This completely compensates for the sparse schema and adds practical invocation details.

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 opens with 'Get leading causes of death with death counts and age-adjusted rates', clearly identifying the specific resource and its primary outputs. It further specifies the NCHS Leading Causes of Death dataset and national/state-level scope, which distinguishes it from sibling mortality-related tools.

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 provides clear context for use: it returns NCHS mortality statistics by cause, year, and state, spanning 1999-2017. It explains optional state/year filters and their defaults, but it does not explicitly mention when not to use this tool or name alternative 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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