Leading causes of death, mortality rates, infant mortality, and drug overdose data
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).
| Name | Required | Description | Default |
|---|---|---|---|
| year | No | ||
| limit | No | ||
| state | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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.
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.
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.
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.
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.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| year | No | ||
| state | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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.
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.
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.
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.
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.
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.
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).
| Name | Required | Description | Default |
|---|---|---|---|
| year | No | ||
| limit | No | ||
| state | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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.
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.
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.
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.
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.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| year | No | ||
| cause | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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.
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.
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.
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.
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.
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.
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).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| state | No | ||
| cause_group | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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.
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.
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.
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.
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.
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.
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County and tract-level health outcomes, behaviors, and preventive services from CDC PLACES
Medicare spending, chronic conditions, hospital quality, readmissions, and enrollment
SVI scores and theme breakdowns by county and tract
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Add one secure layer between your agents and this server.
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.
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.
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.
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.