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AlpineDataWorks Intelligence Server

Retirement Intelligence Package

adw.adw_p03
Read-only

Returns a 0-100 retirement-suitability score for any US county (composite of 8 AlpineDataWorks place-intelligence layers spanning health, cost, climate risk, and livability, joined on county_fips) with composite_score, per-layer component_scores, ranked drivers, and coverage. Call when the user asks whether a place is a good spot to retire or compares retirement destinations, or when timing a relocation, retirement home purchase, or senior-housing market entry. 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

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, so no safety disclaimer is needed. The description adds useful behavioral context: it returns composite_score, per-layer component_scores, ranked drivers, coverage, and states 'Updates: on source cadence.' This goes beyond the annotations and gives the agent a sense of output structure and freshness, though it does not explain how a county is selected.

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 concise, about three sentences, and packs in purpose, composition, use cases, and update cadence. The only slight issue is the vague closing 'Updates: on source cadence,' but overall it is appropriately sized and front-loaded.

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

Completeness2/5

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

While the description explains return fields (composite_score, component_scores, drivers, coverage), it completely fails to address how the target county is specified. With no county_fips parameter in the schema and no mention of a prerequisite or context-setting tool, the tool cannot be correctly invoked for a specific place. This is a critical gap for a geographic-scored tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% for the only parameter (days), so the baseline should be 3. However, the description promises a score 'for any US county' but provides no parameter or explanation of how to specify the county. The sole parameter (days) is an optional history toggle, not the core input. This makes it impossible for the agent to correctly invoke the tool for a specific location.

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 it returns a 0-100 retirement-suitability score for any US county, with a specific verb ('Returns') and resource ('retirement-suitability score'). It also lists the composite structure (8 layers) and distinguishes itself from generic place-intelligence tools by focusing on retirement suitability.

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 says 'Call when the user asks whether a place is a good spot to retire or compares retirement destinations, or when timing a relocation, retirement home purchase, or senior-housing market entry.' This gives clear, specific use cases, though it does not mention when not to use it or name alternative tools.

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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