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AlpineDataWorks Intelligence Server

Housing Affordability Index

adw.adw_500
Read-only

Returns a 0-100 county-level housing affordability score (local home prices and rents scaled against household incomes) with affordability_score, price_to_income and rent_to_income sub-components, national_percentile, county_fips, and methodology_version for all 3,222 US counties. Call when the user asks how affordable housing is in a county or how markets compare on cost burden, or when timing relocation, site-selection, geographic-pay, or housing-investment decisions. Updates: on source cadence.

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 declare readOnlyHint=true, and the description adds useful context beyond that: it returns a snapshot for all counties, includes named sub-components, and notes the update cadence. It does not fully describe behavior like the days parameter's Gold-tier requirement, but that is covered in the schema, so the description adds enough context without contradicting 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 a single, information-dense paragraph in three sentences. The first sentence packs the core output and scope, the second gives clear use cases, and the third notes the update cadence. Every sentence earns its place, though the long field enumeration makes it slightly less scannable than a two-sentence version.

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?

Despite having no output schema, the description enumerates the return fields (affordability_score, price_to_income, rent_to_income, national_percentile, county_fips, methodology_version), defines the score range, explains county-level coverage, and lists use cases. Combined with the schema's detailed parameter description, the tool is fully understandable and invokable.

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 has 100% parameter coverage with a well-described optional `days` parameter, so the baseline is 3. The description does not mention parameters, but it doesn't need to; the schema already explains the behavior and tier restriction. The description adds no extra parameter semantics beyond what the schema provides.

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 precisely names the resource: a 0-100 county-level housing affordability score with sub-components and coverage of all 3,222 US counties. It clearly differentiates this tool from the many sibling adw.* indicators by specifying the housing-cost-burden domain and the exact metrics included.

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 gives explicit, actionable use cases: 'Call when the user asks how affordable housing is in a county or how markets compare on cost burden', plus relocation, site-selection, geographic-pay, and housing-investment decisions. It does not explicitly name alternative tools or when not to use it, but the guidance is clear and sufficient for an indicator-lookup tool.

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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