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AlpineDataWorks Intelligence Server

by com.alpinedataworks

US Respiratory Illness Activity Index

adw.adw_572
Read-only

Returns a 0-100 US respiratory-illness activity score (latest weekly national ARI share of ED visits, CDC NSSP, keyless) with trend, season_high_low band, vs_4wk_avg, ~12-week recent_weeks history, and COVID/flu/RSV component_pathogens_pct split. Call when the user asks about flu, COVID, RSV, or "sick season" levels right now, or when timing healthcare staffing, OTC/test-kit inventory, or absenteeism planning. Updates: weekly.

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

A4.7/5.0
Behavior5/5

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

Annotations declare readOnlyHint=true, and the description adds useful behavioral context: weekly update cadence, keyless access, and the Gold-tier requirement for daily history. It also explains what the returned data includes and the distinction between the default snapshot and optional history. No contradiction with 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?

Two sentences: the first packs the core operational details, the second provides usage guidance. No fluff, front-loaded with the most important information, and every phrase contributes value.

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

Completeness5/5

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

Despite having no output schema, the description enumerates the return fields richly (score, trend, season band, vs_4wk_avg, recent weeks, pathogen split). It includes data frequency, historical depth, tier requirement, and multiple real-world use cases. For a simple read-only tool with one optional parameter, this is fully complete.

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 single 'days' parameter is fully documented in the schema (coverage 100%), including its optional nature, range, purpose, and tier restriction. The main description does not add parameter-level detail beyond referencing '~12-week recent_weeks history' in the default output, so the schema carries the burden. 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 opens with a clear verb 'Returns' and specifies the exact resource: a 0-100 US respiratory-illness activity score. It enumerates components (trend, season band, vs_4wk_avg, history, pathogen split) and names the data source (CDC NSSP), making it unambiguous and distinct from any sibling tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to call: 'when the user asks about flu, COVID, RSV, or sick season levels right now' and extends to use cases like healthcare staffing, inventory, and absenteeism planning. This gives clear contextual triggers without needing to mention alternatives.

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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