CDC Mortality (WONDER)

get_drug_overdose_deaths

Get provisional drug overdose death data by state.

Returns provisional counts and rates of drug overdose deaths from the
National Vital Statistics System (NVSS). Includes data on opioid,
synthetic opioid, and other drug-involved deaths. Updated monthly.

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

A4.4/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains default behavior for state and year filters, case-insensitivity, and limit constraints (default 25, max 1000), as well as the provisional nature and monthly updates. This goes beyond a simple 'Get data' statement, though it could also mention authentication or response format, but output schema likely covers the latter.

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 and front-loaded with the core purpose, followed by concise details and a clearly formatted Args section. Every sentence adds value—data source, included drug types, update frequency, and parameter behaviors—without unnecessary fluff.

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 tool with three optional parameters and an existing output schema, the description provides all needed context: what data is returned (provisional counts/rates), the source, update cadence, and parameter defaults. No critical information is missing for a typical filtering use case, and the output schema handles return structure.

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 has zero description coverage (0%), so the description must fully compensate. It thoroughly documents all three parameters: state (with examples, case-insensitive, defaults to all), year (defaults to all years), and limit (default 25, max 1000). This is complete and clear, exceeding baseline compensation.

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 starts with a specific verb and resource: 'Get provisional drug overdose death data by state.' It further specifies the data source (NVSS), types of drugs (opioid, synthetic opioid, other), and update frequency, clearly distinguishing it from sibling tools like get_infant_mortality or get_provisional_mortality.

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

Usage Guidelines3/5

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

The description clearly implies when to use this tool (for drug overdose death data) by specifying the resource and scope. However, it does not explicitly mention alternatives or when not to use it, such as differentiating from get_provisional_mortality. There is no direct comparison or exclusion, so guidance is implied rather than explicit.

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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