CMS Medicare Data

get_hospital_readmissions

Get 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).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No
`state`	No
`hospital_name`	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 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.

Conciseness5/5

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.

Completeness4/5

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.

Parameters5/5

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.

Purpose5/5

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.

Usage Guidelines4/5

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.

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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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