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AlpineDataWorks Intelligence Server

US Wage vs Business-Formation Divergence

adw.adw_609
Read-only

Returns a 0-100 US wage-vs-business-formation divergence score (YoY spread of FRED avg hourly earnings CES0500000003 vs Census business applications BABATOTALSAUS, z-scored on a 2007 baseline) with trend, z_score, composite_recent_avg, composite_baseline_avg, and points_analyzed. Call when the user asks about wage inflation, SMB viability, labor-cost squeeze, or new-business formation, or when timing SMB credit tightening, payroll-segment forecasts, or small-business exposure decisions. 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

A4.2/5.0
Behavior4/5

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

Annotations already provide readOnlyHint=true. The description adds useful behavioral context: data sources, baseline year, update frequency ('Updates: weekly'), and the exact output fields. It does not contradict annotations and enriches the agent's understanding of what the read-only operation returns.

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 two sentences, front-loads the core purpose, then provides usage guidance and update frequency. No redundant wording or unnecessary detail; every clause adds value.

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 enumerates the return fields, lists concrete use cases, and notes the weekly update cadence. It lacks any mention of score interpretation (e.g., what high vs low divergence means) or fallback behavior if history is requested without Gold tier, but the latter is covered in the schema. For a single-parameter read-only indicator, this is quite 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?

Schema coverage is 100% for the single optional 'days' parameter, including its description, bounds, and Gold tier requirement. The tool description does not need to add parameter details; it already implies the historical-series option by describing the snapshot behavior. Baseline 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 states the tool returns a specific 0-100 divergence score with clear data sources (FRED CES0500000003 vs Census BABATOTALSAUS) and a precise calculation method (YoY spread z-scored on a 2007 baseline). It is a specific verb+resource+scope that clearly distinguishes this tool from generic siblings.

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 explicitly lists use cases ('when the user asks about wage inflation, SMB viability...') and timing contexts ('SMB credit tightening, payroll-segment forecasts, small-business exposure decisions'). It does not mention when not to use the tool or name alternative tools, so it falls short of a 5.

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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