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AlpineDataWorks Intelligence Server

Hospital Access Index

adw.adw_522
Read-only

Returns a 0-100 hospital access score for any of 3,222 US counties (hospital facility density and staffed-bed capacity relative to population, from federal provider data) with access_score, national_percentile, drivers, county_fips, and methodology_version. Call when the user asks about hospital access, healthcare deserts, or county care capacity, or when timing site selection, network-adequacy filings, or relocation and expansion decisions. Updates: on source cadence.

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

A4.1/5.0
Behavior4/5

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

Annotations declare readOnlyHint=true and openWorldHint=false, so the agent knows this is a safe, bounded read operation. The description adds value beyond these annotations by specifying the data source (federal provider data), coverage of 3,222 counties, return fields, and update cadence. It does not contradict the annotations.

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 three concise sentences, each earning its place: the first defines the output and scope, the second provides use cases, the third states update frequency. It is front-loaded and contains no redundant or filler content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers output fields, data source, and use cases, which is good for a read-only tool with a single optional parameter. However, the lack of any county parameter in the schema despite claiming county-level output is a notable completeness gap that could lead to incorrect invocation. Also, the meaning of 'drivers' and the interpretation of the 0-100 scale are not explained, and there is no output schema to compensate.

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 input schema covers the only parameter 'days' fully, including its bounds and Gold-tier requirement for history, so the description does not need to repeat it. However, the description's claim of 'any of 3,222 US counties' is not reflected in the schema—there is no county input parameter—creating ambiguity about how the county is selected. The description adds no further parameter semantics beyond the 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 uses a specific verb ('Returns') with a precise resource ('0-100 hospital access score'), a defined scope ('any of 3,222 US counties'), and lists the key output fields. It clearly distinguishes this from sibling health/county tools by focusing on hospital access density and bed capacity.

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 gives explicit 'Call when' scenarios covering both user questions ('hospital access, healthcare deserts, county care capacity') and decision contexts ('site selection, network-adequacy filings, relocation'). It does not mention when not to use it or name alternatives, but the use cases are sufficiently clear and 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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