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AlpineDataWorks Intelligence Server

by com.alpinedataworks

US Healthcare Admin Overhead

adw.adw_404
Read-only

Returns a 0-100 US healthcare administrative-overhead gap index (CMS Medicare provider cost and utilization data; administrative-cost growth vs. care-delivery growth, time series to 2009) with gap_index, admin_burden_trend, percentile_rank, confidence, and methodology_version. Call when the user asks about hospital admin overhead, billing or payer friction, or inpatient margin compression, or when timing hospital cost-reduction programs, RCM investments, or provider-credit reviews. Updates: quarterly.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoOptional: return a daily HISTORY series of the last N days (up to 5 years of real archived data) instead of the current snapshot. History requires Gold tier; without it, the current snapshot is returned.

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already set readOnlyHint=true, and the description adds meaningful behavior: it returns either a current snapshot or a historical daily series, requires Gold tier for history, and is updated quarterly. This goes beyond the annotation without contradiction.

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?

Three sentences: the first front-loads the return value and key fields, the second lists specific use cases, and the third indicates update frequency. Each sentence earns its place with no filler or repetition.

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 description covers purpose, exact data source, output field names, time-series limit, use cases, update cadence, and the history behavior. With no output schema, listing these fields compensates well, though exact response envelopes and data types are not specified.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description covers 100% of the parameter (days), including range, purpose, and tier behavior. The tool description does not add additional parameter meaning, so a baseline of 3 is appropriate.

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 specifies a 0-100 US healthcare administrative-overhead gap index, names the data source (CMS Medicare), lists all output fields, and distinguishes this tool from the many numeric siblings by topic and scope.

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?

It explicitly lists when to call the tool ('hospital admin overhead, billing or payer friction, inpatient margin compression, RCM investments, provider-credit reviews') and states update frequency. It lacks when-not-to-use guidance or alternative tool names, but the provided triggers are highly specific.

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

B3.3/5.0
Disambiguation1/5

With 318 tools named adw.adw_###, agents cannot tell them apart without reading full descriptions. Multiple tools cover the same domain (e.g., at least three USD strength scores: adw_055, adw_250, adw_580; four supply-chain stress scores: adw_009, adw_019, adw_020, adw_547), making misselection highly likely.

Naming Consistency3/5

The vast majority follow a consistent numeric ID pattern (adw.adw_###), but a small set breaks this with descriptive snake_case names (adw.catalog, adw.sample, adw.county_cancer, etc.). The numeric IDs are predictable but convey no semantic meaning, mixing with the few named tools and creating moderate inconsistency.

Tool Count1/5

318 tools is far beyond any reasonable scope for an intelligence server; even the largest sophisticated APIs rarely exceed 50. This extreme count suggests poor curation and will overwhelm agents with choice, making efficient tool selection impractical.

Completeness3/5

The server covers an extremely broad range of domains (crypto, macro, supply chain, healthcare, climate, county demographics), and includes discovery tools like adw.catalog and adw.sample. However, the surface is redundant and not systematically complete—many overlapping indices exist while other potentially valuable operations (e.g., raw data export, historical trend queries) are missing, leaving moderate gaps.

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