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AlpineDataWorks Intelligence Server

by com.alpinedataworks

Commercial Real-Estate Stress Index

adw.adw_052
Read-only

Returns a 0-100 commercial real estate lending stress score (FRED bank CRE loan delinquency rate, 70/30 level-plus-momentum z-score blend, quarterly since 1992) with stress_label, trend, confidence, top_drivers, delinquency rate, and momentum/composite z-scores. Call when the user asks about CRE stress, commercial real estate delinquencies, bank credit quality, or property lending risk, or when timing CRE loan-concentration reviews, credit-committee escalations, or exposure decisions. Updates: quarterly.

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?

The description adds transparency about data source (FRED delinquency rate), blending methodology (70/30 level-plus-momentum z-score), frequency (quarterly since 1992), and output fields. The readOnlyHint annotation already indicates safety, and the description does not contradict it.

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 sentences, front-loaded with the core output, then usage context. Every sentence serves a purpose; no filler.

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 no-output-schema tool, the description enumerates the key returned components (stress_label, trend, confidence, top_drivers, delinquency rate, z-scores), states update frequency, and provides explicit use cases. This is complete for an agent to select and invoke the tool.

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 covers the single `days` parameter 100% with a clear description. The description does not mention this parameter, but the schema is sufficient, so 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?

The description states a specific verb+resource: 'Returns a 0-100 commercial real estate lending stress score' with methodology and output fields. It clearly distinguishes from siblings by specifying CRE stress context in the usage sentence.

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 CRE stress, commercial real estate delinquencies, bank credit quality, or property lending risk, or when timing CRE loan-concentration reviews, credit-committee escalations, or exposure decisions.' However, no when-not-to-use or alternative tool names are given.

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