CMS Medicare Data

get_hospital_quality

Get 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).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`city`	No
`limit`	No
`state`	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?

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.

Conciseness5/5

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.

Completeness4/5

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.

Parameters5/5

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.

Purpose5/5

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.

Usage Guidelines4/5

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.

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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

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