CDC Mortality (WONDER)

get_provisional_mortality

Get most recent provisional mortality data including COVID and other causes.

Returns provisional death counts from the NVSS, covering major cause
groups including COVID-19, respiratory diseases, circulatory diseases,
and more. Data is updated weekly and covers the most recent periods.

Args:
    state: Filter by state/jurisdiction name (e.g. 'New York', 'Florida').
        Case-insensitive. Returns all jurisdictions if not specified.
    cause_group: Filter by cause group (e.g. 'COVID-19', 'Respiratory',
        'Circulatory', 'Malignant neoplasms'). Partial match supported.
        Returns all cause groups if not specified.
    limit: Maximum number of records to return (default 50, max 1000).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No
`state`	No
`cause_group`	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, the description takes on the full burden of behavioral disclosure. It adds useful context: data is provisional, updated weekly, state matching is case-insensitive, cause_group supports partial matches, and unspecified filters return all values. These details help the agent predict behavior, though it does not mention potential limitations like incomplete or revised data.

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 concise summary paragraph followed by a clear Args list. Each parameter description is compact yet informative, avoiding redundancy. Every sentence adds value, and the format makes scanning easy for an AI agent.

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?

Given the tool's moderate complexity, the description covers purpose, data source, update cadence, and all parameter semantics. An output schema exists, so the lack of detailed return-value documentation is acceptable. The description fully equips an agent to select and invoke the tool correctly. Minor gaps like multi-state filtering are not critical for this 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 has 0% description coverage, so the description must fully explain each parameter. It does so admirably: state includes example names and case-insensitivity; cause_group includes examples and partial matching; limit gives default and maximum. This goes beyond basic type information and provides actionable guidance for selecting values.

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 a specific verb+resource: 'Get most recent provisional mortality data including COVID and other causes.' It explicitly mentions NVSS, major cause groups, and weekly updates, clearly distinguishing it from sibling tools focused on drug overdose, infant mortality, leading causes, or state-specific final mortality.

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 about when to use the tool: it returns provisional death counts from NVSS, updated weekly, covering the most recent periods. However, it does not explicitly mention alternatives or exclusions, such as 'for final mortality data use get_mortality_by_state,' so it stops short of a full 5.

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

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources

Need Help?