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AlpineDataWorks Intelligence Server

Dollar-Strength Index

adw.adw_055
Read-only

Returns a 0-100 US dollar strength score (composite z of FRED broad trade-weighted index DTWEXBGS: level 60%, 30-day momentum 40%; 50=neutral, higher=stronger; monthly, 16.5yr history) with trend, confidence, top drivers, strength_label, latest index, 30-day momentum %, and composite z. Call when the user asks whether the dollar is strengthening or weakening, about USD/FX/exchange-rate/currency moves, or when timing FX hedges, hedge tenors on foreign payables, or import/export pricing. Updates: monthly.

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 indicate read-only safety. The description adds meaningful context beyond that: the composite z methodology (level 60%, momentum 40%), monthly update frequency, and 16.5-year history. It does not contradict annotations and provides enough behavioral detail for an agent.

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 dense but every clause earns its place: what it returns, methodology, interpretation, when to call, and update frequency. It is organized in a single coherent sentence followed by targeted usage guidance, with no wasted words.

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?

For a moderately complex tool without an output schema, the description covers the core purpose, methodology, output components, interpretation, and use cases. It does not explicitly describe the optional history mode in prose, but the schema covers that, and the description provides enough for correct selection and invocation.

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 fully documents the only parameter 'days' with its type, range, and behavior (returns history series, requires Gold tier). The description itself does not mention this parameter, so it adds no extra semantic value beyond the schema's already-complete coverage.

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 states it returns a 0-100 US dollar strength score with specific components (trend, confidence, top drivers, etc.), distinguishing it from other tools. It names the precise economic measure (FRED broad trade-weighted index) and the output semantics (50=neutral, higher=stronger).

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 when to call it: user asks about dollar strength/weakness, USD/FX/currency moves, or FX hedging/pricing decisions. It does not mention when not to use it or name alternative tools, but the clear use-case list provides strong guidance.

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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