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AlpineDataWorks Intelligence Server

by com.alpinedataworks

Consumer-Credit Stress Index

adw.adw_015
Read-only

Returns a monthly 0-100 US consumer-credit stress score (FRED card delinquency rate + revolving credit YoY, z-scored vs trailing 36 months; 50=neutral, higher=stress; since 1992) with trend, confidence, stress_level, top_drivers, delinquency_rate_pct, revolving_credit_yoy_pct. Call when the user asks about consumer credit stress, card delinquencies, revolving debt, credit binging, or household borrowing, or when timing underwriting tightening, collections pre-staging, or credit-risk alerts. 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

A4.5/5.0
Behavior5/5

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

Annotations already provide readOnlyHint, and the description adds substantial context: data source (FRED), methodology (z-scored, trailing 36 months), interpretation (50=neutral), history since 1992, and update cadence. Exceeds annotation coverage without contradiction.

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 dense but purposeful sentences: the first packs methodology and output fields, the second gives usage triggers and update frequency. No wasted words; front-loaded with the key output.

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?

Without an output schema, the description lists all returned fields. It also covers methodology, data source, update frequency, and usage context. For a single-optional-param indicator tool, this is 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 only optional parameter (days), which is well-documented. The description adds no additional parameter semantics, so the baseline 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?

Clearly states it returns a monthly 0-100 US consumer-credit stress score with specific methodology (z-scored vs trailing 36 months) and output fields. The verb 'Returns' plus the precise resource differentiates it from generic 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?

Provides explicit 'Call when' triggers (consumer credit stress, card delinquencies, etc.) and use cases (underwriting tightening, collections). Missing when-not-to-use or alternative tool mentions, so not 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.

Resources