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AlpineDataWorks Intelligence Server

Fleet Aging Index

adw.adw_545
Read-only

Returns a 0-100 US general-aviation fleet obsolescence score (share of the 242k-airframe US fixed-wing fleet built before 1990, currently ~58%) with pre_1990_share, build_decade_distribution, median_build_decade, and shipment_trend. Call when the user asks about GA fleet age, avionics retrofit or aircraft replacement demand, or aviation aftermarket sizing, or when timing MRO capacity, retrofit product-line, or parts-inventory investments. Updates: annually.

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

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, so the description's read-only nature is not new. However, it adds meaningful behavioral context: 'Updates: annually' discloses data freshness, and the definition of the score (share of 242k airframes built before 1990) clarifies what the number represents. This exceeds the annotation baseline.

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: the first delivers the core function and output fields, the second provides usage context and update frequency. Every sentence earns its place; no filler or redundancy.

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?

For a simple read-only tool with no required parameters and no output schema, the description covers purpose, outputs (field names), usage triggers, and data cadence. The optional 'days' parameter is fully documented in the schema, so the lack of repetition is acceptable. The description is sufficiently complete for an agent to select and invoke correctly.

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 description coverage is 100% for the only parameter ('days'), which is fully specified. The tool description adds no parameter-level detail, so the baseline of 3 applies.

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 specific verb ('Returns') and a precise resource: a '0-100 US general-aviation fleet obsolescence score' with listed output fields. This unique domain (GA fleet age, retrofit demand) clearly distinguishes it from the many sibling tools.

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?

Explicit when-to-use guidance is provided: 'Call when the user asks about GA fleet age, avionics retrofit or aircraft replacement demand, or aviation aftermarket sizing, or when timing MRO capacity, retrofit product-line, or parts-inventory investments.' This enumerates clear contexts, but no exclusions or alternative tools are named, stopping 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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