CDC Mortality (WONDER)

get_infant_mortality

Get infant mortality rates by state and race/ethnicity.

Returns infant mortality data including rates per 1,000 live births,
broken down by state and race/ethnicity. Infant mortality is a key
indicator of community health status used in CHNAs.

Args:
    state: Filter by state name (e.g. 'Ohio', 'Georgia'). Case-insensitive.
        Returns all states if not specified.
    year: Filter by year. Returns all available years if not specified.

Input Schema

TableJSON Schema

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

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.5/5.0

Behavior4/5

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

With no annotations provided, the description carries the transparency burden. It discloses the output (rates per 1,000 live births, breakdowns) and filter defaults (returns all states/years if not specified, case-insensitivity). It does not cover data source or missing-data behavior, but core call behavior is transparent.

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: a clear two-sentence overview followed by a structured Args list. Every sentence adds useful information; no fluff or redundant restatement of the tool name.

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

Completeness4/5

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

Given an output schema exists (so return fields need not be described) and the two simple optional parameters are well documented, the description is nearly complete. It lacks explicit differentiation from sibling tools and does not mention data provenance or caveats, but it is sufficient for correct invocation.

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 input schema provides only titles and defaults with zero description coverage. The description's Args section adds substantial value: case-insensitivity, example state names, and explicit default behavior for both parameters when omitted. This fully compensates for the sparse schema.

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 uses a specific verb ('Get') and identifies the resource ('infant mortality rates') along with the key dimensions (state, race/ethnicity). This clearly distinguishes it from siblings like 'get_mortality_by_state' which would cover broader mortality data.

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 by stating infant mortality is a key community health indicator used in CHNAs, and it explains optional filtering behavior. However, it does not explicitly contrast with alternatives like 'get_mortality_by_state' or 'get_provisional_mortality'.

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

All CDC Mortality (WONDER) tools Inspect

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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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