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AlpineDataWorks Intelligence Server

Population & Business Baseline Package

adw.adw_p24
Read-only

Returns a 0-100 population-and-business baseline score for any US county (composite of 4 AlpineDataWorks V2 place-intelligence layers joined on county_fips) with composite_score, component_scores, drivers, and coverage. Call when the user asks about a county's population denominator, business activity, economic base, or the market size behind a place score, or when timing site-selection, expansion-screening, or per-capita normalization 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

A3.8/5.0
Behavior3/5

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

With readOnlyHint=true already covering safety, the description adds 'Updates: on source cadence' and the composite-layer construction, which is useful context. However, it does not disclose how the county is determined (no county parameter in schema) or any potential errors/edge cases, so transparency is adequate but not rich.

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?

The description is concise and front-loaded: two sentences plus a short updates note. It wastes no words, stating the purpose, output, use cases, and data freshness efficiently.

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?

The description claims the tool works for 'any US county' but the input schema only has an optional 'days' parameter — there is no county parameter or explanation of how the county is specified (e.g., via context or other means). Additionally, return fields like 'drivers' and 'coverage' are named but not defined, and there's no output schema to clarify. This is a significant gap for a tool that is otherwise simple.

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 only parameter 'days' is fully described in the input schema (100% coverage), so the description does not need to add parameter semantics. It adds no extra meaning beyond the schema, but the schema already provides the necessary details, so the baseline score 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's function: 'Returns a 0-100 population-and-business baseline score for any US county' with composite_score, component_scores, drivers, and coverage. It distinguishes itself from sibling tools by specifying the composite of 4 place-intelligence layers joined on county_fips, making it unique among the many adw_pXX 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?

The description explicitly provides use cases: 'Call when the user asks about a county's population denominator, business activity, economic base... or when timing site-selection, expansion-screening, or per-capita normalization decisions.' This gives clear context for when to use, though it does not mention when not to use 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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