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AlpineDataWorks Intelligence Server

by com.alpinedataworks

Auto-Market Affordability Index

adw.adw_042
Read-only

Returns a 0-100 auto affordability score (New Vehicle CPI YoY + 48-month auto loan rate z-scores, 0.6/0.4 weighted vs 36-month window, FRED monthly since 1983) with trend, confidence, top_drivers, affordability_label, vehicle_price_z, auto_rate_z, and composite_z. Call when the user asks about car affordability, new vehicle prices, auto loan rates, or consumer auto financing costs, or when timing auto lending credit tightening, underwriting thresholds, or vehicle purchase decisions. 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

A4.6/5.0
Behavior5/5

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

Annotations only say readOnlyHint=true and openWorldHint=false. The description adds substantial context: the exact weighted formula, the data source (FRED monthly since 1983), update frequency, output field list, and the Gold tier requirement for historical data. This goes well beyond 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but every sentence carries value: formula, outputs, use cases, update frequency. It is front-loaded with the core functionality. Slightly long but not wasteful.

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 tool with no output schema, the description enumerates all output fields (trend, confidence, top_drivers, etc.), describes the data source and update cadence, and gives the Gold tier caveat for history. It feels complete for the tool's complexity.

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 sole 'days' parameter, and its schema description is already detailed. The tool description does not add extra parameter semantics, but the baseline of 3 applies because the schema carries the burden.

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 starts with a specific verb ('Returns') and resource ('0-100 auto affordability score'), and even explains the underlying formula. This unambiguously distinguishes the tool from the many sibling adw tools.

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: 'Call when the user asks about car affordability, new vehicle prices, auto loan rates, or consumer auto financing costs, or when timing auto lending credit tightening...' This is strong use-case 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.

Resources