Medicare spending, chronic conditions, hospital quality, readmissions, and enrollment
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Average 4.3/5 across 5 of 5 tools scored.
Each tool targets a distinct Medicare dataset: chronic conditions, hospital quality, hospital readmissions, enrollment, and spending. There is no overlap in purpose or output, making tool selection unambiguous.
All tool names follow a consistent 'get_' prefix followed by a clear domain noun (e.g., get_chronic_conditions, get_hospital_quality). This predictable verb_noun pattern holds across all tools with no exceptions.
With 5 tools, the server is well-scoped for a focused Medicare data access API. Each tool covers a major data category, and the number is neither too few to be useful nor too many to be unwieldy.
The tool set covers core Medicare data domains: conditions, hospital performance, readmissions, enrollment, and spending. Minor gaps exist (e.g., no provider directory or drug spending data), but the current set addresses primary use cases without significant dead ends.
get_chronic_conditionsAInspectGet prevalence of chronic conditions among Medicare beneficiaries.
Returns state-level data on 21 chronic conditions including diabetes,
heart failure, COPD, depression, Alzheimer's, and more. Useful for
understanding disease burden by geography.
Args:
state: Two-letter US state abbreviation (e.g. 'CA', 'TX').
condition: Chronic condition name to filter by (e.g. 'diabetes',
'heart failure', 'COPD', 'depression').
year: Year of data (e.g. 2022).
limit: Maximum number of records to return (default 50, max 1000).
| Name | Required | Description | Default |
|---|---|---|---|
| year | No | ||
| limit | No | ||
| state | No | ||
| condition | 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 must carry the burden. It discloses that data is state-level and covers 21 conditions, adding value beyond the tool name. However, it does not explicitly state read-only behavior, note any edge cases, or describe response characteristics beyond the schema.
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 efficiently organized with a concise summary paragraph and a structured Args list. Every sentence contributes meaning, with no redundant or vague filler.
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 tool has 4 parameters and an output schema, and the description covers all parameters and the data scope (state-level, 21 conditions). Minor gaps like an exhaustive list of valid condition names or case-sensitivity rules exist, but overall it is sufficient for an agent to invoke.
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, but the description includes an Args section explaining each parameter with examples, defaults, and limits. This fully compensates for the schema's lack of descriptions.
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 a clear resource 'prevalence of chronic conditions among Medicare beneficiaries.' It also distinguishes from sibling tools like get_hospital_quality or get_hospital_readmissions by focusing on chronic condition prevalence.
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 states 'Useful for understanding disease burden by geography,' providing clear context for when to use. It does not explicitly name alternative tools or exclusions, but the purpose and sibling names make the intended use clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_hospital_qualityAInspectGet hospital quality star ratings and general information.
Returns hospital quality data including overall star ratings,
location details, and hospital type. Filter by state or city.
Args:
state: Two-letter US state abbreviation (e.g. 'CA', 'TX').
city: City name to filter by (e.g. 'Chicago', 'Houston').
limit: Maximum number of records to return (default 50, max 1000).
| Name | Required | Description | Default |
|---|---|---|---|
| city | No | ||
| limit | No | ||
| state | 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 must convey behavioral traits. It discloses the return content, filtering options, and limit behavior (default and max). However, it does not explicitly state that this is a read-only safe operation or what happens when no filters are provided, leaving some ambiguity.
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 first sentence, a brief summary of return data, and an Args list. Every sentence adds useful information without redundancy or filler.
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 simplicity and the presence of an output schema, the description covers the essential aspects: purpose, filtering, parameters, and return content. It does not discuss edge cases or explicit alternatives, but overall it is sufficiently complete for an agent to understand the tool's role.
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%, but the description fully compensates with an Args section that explains each parameter: state (two-letter abbreviation with examples), city (name with examples), and limit (default 50, max 1000). This adds significant semantic value beyond the bare 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 clearly states the tool gets hospital quality star ratings and general information, listing specific data types (star ratings, location, hospital type). This distinguishes it from sibling tools like get_hospital_readmissions or get_medicare_spending, which focus on different aspects.
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: this tool is for hospital quality data and can be filtered by state or city. It does not explicitly mention alternatives or when not to use it, but the purpose is specific enough that an agent can infer appropriate use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_hospital_readmissionsAInspectGet 30-day hospital readmission rates by hospital.
Returns hospital-level readmission data including excess readmission
ratios and predicted/expected readmission rates for conditions like
heart attack, heart failure, and pneumonia.
Args:
state: Two-letter US state abbreviation (e.g. 'CA', 'TX').
hospital_name: Hospital name or partial name to search for.
limit: Maximum number of records to return (default 50, max 1000).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| state | No | ||
| hospital_name | 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 that the tool returns hospital-level readmission data including excess readmission ratios and predicted/expected rates, and it documents parameter constraints (limit default/max). However, it does not explicitly state that the operation is read-only, nor does it mention data vintage, potential errors, or any special access requirements. It adds useful return-value context but not a comprehensive behavioral profile.
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 compact and well-structured: a one-line summary, a sentence describing return content, and a bullet-style Args list. Every sentence earns its place, and the key information is front-loaded. No redundancy or filler.
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 existence of an output schema, the description does not need to detail return fields exhaustively. It covers purpose, the specific data type (readmission rates for conditions), and parameter semantics. It is missing sibling-tool differentiation and explicit usage scenarios, which would round out the context, but for a straightforward data retrieval tool with optional parameters, it is largely complete.
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 0% description coverage, so the description must fully compensate, and it does. The Args section explains each parameter: state as a two-letter abbreviation, hospital_name as a partial-name search, and limit with default and maximum values. This adds significant meaning beyond the bare schema properties, which only list names and types.
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 begins with a specific verb and resource: 'Get 30-day hospital readmission rates by hospital.' It clearly defines the tool's scope (readmission rates, 30-day, hospital-level) and differentiates it from sibling tools like get_hospital_quality or get_chronic_conditions by focusing on readmissions specifically.
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 states what data it retrieves (30-day readmission rates) and the context (hospital-level, conditions like heart attack and pneumonia). However, it does not explicitly mention alternatives or when not to use it relative to sibling tools, so it lacks exclusionary guidance. The clear scope still provides adequate context for an agent to select this tool for readmission-focused queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_medicare_enrollmentAInspectGet Medicare enrollment data by state and county.
Returns enrollment counts including total beneficiaries, Original
Medicare vs Medicare Advantage enrollment, and Part D enrollment.
Useful for understanding Medicare population by geography.
Args:
state: Two-letter US state abbreviation (e.g. 'CA', 'TX').
year: Year of enrollment data (e.g. 2022).
limit: Maximum number of records to return (default 50, 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?
No annotations are present, so the description carries the burden. It discloses return contents, defaults, and max limit, which is helpful. However, it omits any mention of required vs optional parameters, error behavior, or what happens if 'state' is omitted, despite the schema showing no required params. It also mentions 'county' in the description but no county parameter exists, which is a minor inconsistency.
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 two concise paragraphs followed by a tight Args list. It front-loads the purpose and return value, then gives precise parameter details. No fluff or redundant lines.
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?
An output schema exists, so the description doesn't need to detail return fields. The description covers purpose, return value, and parameter semantics. The only gap is the state/county mismatch and the lack of guidance on optional parameters, which is a minor omission given the tool's simplicity.
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%, so the description must compensate, which it does thoroughly. The Args section explains the format for 'state' ('Two-letter US state abbreviation'), provides an example for 'year', and states the default and max for 'limit'. All three parameters are given meaningful context beyond the raw 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 specifies an exact action ('Get Medicare enrollment data'), resource ('Medicare enrollment data'), and scope ('by state and county'). It clearly distinguishes from siblings like get_chronic_conditions or get_hospital_quality by focusing on enrollment counts and geographic granularity.
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 phrase 'Useful for understanding Medicare population by geography' provides clear context for when this tool is appropriate. It doesn't explicitly mention exclusions or alternatives, but given the sibling tool names are topically different, the intended use is obvious.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_medicare_spendingAInspectGet Medicare spending per beneficiary by geographic area.
Returns geographic variation in Medicare spending including per-capita
costs and total spending amounts. Filter by state, county, or year.
Args:
state: Two-letter US state abbreviation (e.g. 'CA', 'TX').
county: County name to filter by (e.g. 'Los Angeles').
year: Year of spending data (e.g. 2022).
limit: Maximum number of records to return (default 50, max 1000).
| Name | Required | Description | Default |
|---|---|---|---|
| year | No | ||
| limit | No | ||
| state | No | ||
| county | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses return values (per-capita costs, total spending) and the limit parameter's default and max. However, it does not mention behavior when no filters are applied, how multiple filters interact, or pagination details. This is a moderate level of disclosure, hence a 3.
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 brief purpose statement, a one-sentence summary of return values, and a clear list of arguments. It front-loads the most important information and contains no wasted words.
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 covers the tool's purpose, return data, parameters, and limit behavior. Since an output schema exists, it need not detail return values further. Minor gaps like unspecified filter interactions exist, but overall it is sufficiently complete for a straightforward data retrieval 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 fully compensates by explaining each parameter with examples: state abbreviation, county name, year, and limit with default and max. This adds significant meaning beyond the bare schema and earns top score.
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 explicitly states 'Get Medicare spending per beneficiary by geographic area', which clearly identifies the tool's function with a specific verb and resource. It distinguishes itself from sibling tools that focus on chronic conditions, hospital quality, readmissions, and enrollment by focusing specifically on spending.
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 implies usage context by stating what the tool returns and offering filter options ('Filter by state, county, or year'). It does not explicitly name alternatives or exclusions, but the purpose is clear enough to guide selection. This falls short of a 5 but is above a 3 because context is present.
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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