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AlpineDataWorks Intelligence Server

Logistics Intelligence Package

adw.adw_p13
Read-only

Returns a 0-100 county freight-and-movement network strength score (composite of 6 AlpineDataWorks place-intelligence layers joined on county_fips) with composite_score, component_scores, per-layer drivers, and coverage. Call when the user asks about logistics strength, freight access, distribution-network quality, or how strong a county's movement network is, or when timing site selection, warehouse placement, or market-entry 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

A4.1/5.0
Behavior4/5

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

Annotations already indicate read-only and closed-world behavior. The description adds meaningful context: output structure (composite_score, component_scores, per-layer drivers, coverage), the county_fips join, and update cadence. It also implies a snapshot default without going into detail, but does not contradict 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 dense but efficient, front-loading the core output in the first sentence and following with clear usage triggers and update cadence. No filler or redundant restatement of the name/title.

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?

Given no required parameters and no output schema, the description does a good job enumerating return fields and use cases. It does not explain how the target county is specified, but this may be implicit in the broader adw package context; otherwise, the description is sufficient for a read-only intelligence lookup.

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 sole parameter 'days' is fully documented in the schema, including the history behavior and Gold tier requirement. Schema coverage is 100%, so the description need not add parameter details; it provides no additional parameter meaning beyond the schema, which is acceptable.

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 county freight-and-movement network strength score, names the output components, and describes it as a composite of 6 layers. This is a specific verb-resource pairing that distinguishes it from the many sibling 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?

Explicitly lists trigger scenarios: 'Call when the user asks about logistics strength, freight access, distribution-network quality... site selection, warehouse placement, or market-entry decisions.' It does not mention exclusions or alternative tools, but the stated use cases are concrete and actionable.

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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