CMS Medicare Data

get_chronic_conditions

Get 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).

Input Schema

TableJSON Schema

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

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.3/5.0

Behavior3/5

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.

Conciseness5/5

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.

Completeness4/5

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.

Parameters5/5

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.

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 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.

Usage Guidelines4/5

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.

All CMS Medicare Data 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

A4.4/5.0

Disambiguation5/5

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.

Naming Consistency5/5

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.

Tool Count5/5

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.

Completeness4/5

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.

Resources

Need Help?