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AlpineDataWorks Intelligence Server

by com.alpinedataworks

School Quality Index

adw.adw_502
Read-only

Returns a 0-100 school quality score for any of 3,222 US counties (composite of public K-12 outcome and resource measures — proficiency, graduation, staffing, attainment) with school_quality_score, national_percentile, drivers, confidence, and methodology_version. Call when the user asks about local school quality, district strength, education-driven demand, or family relocation, or when timing residential site selection, homebuilding product mix, or corporate relocation 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

A3.7/5.0
Behavior3/5

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

Annotations already declare read-only and non-open-world behavior. The description adds useful context about update cadence and output composition, but it does not disclose the ambiguity around county selection or other limitations. No contradiction with annotations.

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?

Three dense sentences: the first packs scope, composition, and return fields; the second lists concrete use cases; the third states update cadence. No filler or redundancy.

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 includes return fields and use cases but critically fails to explain how the county is selected. With no output schema and only an optional days parameter, an agent cannot confidently invoke this tool for a specific county, which is a major completeness gap.

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?

The only schema parameter, days, is fully described in the schema itself, so the description need not repeat it. However, the description implies a county selection concept not present in the schema, which is misleading and creates an invocation ambiguity that the description does not resolve.

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?

Clearly states it returns a 0-100 school quality score for 3,222 US counties, including the composite measures and return fields. This is specific and distinguishes the tool from generic data lookups, though it raises a question about how county selection occurs.

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?

Provides explicit when-to-use scenarios: school quality questions, district strength, education-driven demand, family relocation, and residential/corporate site selection. It lacks when-not-to-use guidance and alternatives, and it does not explain how the county is specified given only an optional days parameter.

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.

Resources