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AlpineDataWorks Intelligence Server

Global Real Estate Affordability Score

adw.adw_005
Read-only

Returns a 0-100 global real estate affordability score (higher = more affordable; normalized price-to-income and rent-to-income from FRED housing and World Bank data, monthly since 1963) with affordability_score, price_to_income_ratio, rent_to_income_ratio. Call when the user asks about housing affordability, home prices vs income, rent burden, or housing market valuation, or when timing real estate acquisitions, multifamily underwriting, or rental-demand investment screens. 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

A4.1/5.0
Behavior4/5

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

The annotations already declare readOnlyHint=true, and the description aligns by describing a read operation. It adds valuable context beyond annotations, such as the data being normalized, monthly since 1963, and updated monthly, which helps set expectations about the data's nature and freshness.

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 information-dense but not overly long. It front-loads the core return value and interpretation, then provides usage context and update frequency. The two sentences are somewhat long but each adds necessary detail without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the return fields, interpretation, data sources, update frequency, and usage scenarios. While the optional 'days' parameter is not mentioned in the description, it is fully documented in the schema. The tool is simple with one optional param, and the description is sufficiently complete for an agent to select and invoke it 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?

The schema description coverage is 100% for the single optional parameter 'days', which thoroughly explains the history feature and Gold tier requirement. The tool description does not add parameter-level details, but the schema already carries the full burden, so the baseline of 3 is appropriate.

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 states the tool returns a 0-100 global real estate affordability score with specific components (price-to-income and rent-to-income) and data sources (FRED and World Bank). It uses a specific verb 'Returns' and distinguishes itself from siblings by describing exactly what it measures.

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 explicitly states when to call the tool: 'Call when the user asks about housing affordability, home prices vs income, rent burden, or housing market valuation, or when timing real estate acquisitions, multifamily underwriting, or rental-demand investment screens.' It does not mention alternatives or when not to use it, but the context is clear.

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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