AlpineDataWorks Intelligence Server

Power Grid Index

adw.adw_514

Read-only

Returns a 0-100 power-grid robustness score for any of 3,222 US counties (composite of outage burden, capacity margin, and hazard exposure from federal reliability and storm data) with grid_score, percentile_rank, driver sub-scores, county_fips, and methodology_version. Call when the user asks about grid reliability, blackout risk, or local power resilience, or when timing site selection, backup-power investment, or continuity planning for a specific county. Updates: on source cadence.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`days`	No	Optional: 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

A3.9/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, and the description adds update cadence, data sources (federal reliability and storm data), and output composition. This is useful context beyond the annotations, though it omits details like rate limits or authorization beyond the Gold-tier mention in the schema.

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 front-loaded with the core return value and packs in output fields, use cases, and update cadence without unnecessary words. Each sentence carries meaningful information.

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?

Despite listing output fields and use cases, the description fails to explain how the target county is specified. The presence of only a 'days' parameter means an agent cannot actually select among the 3,222 counties, making the tool definition incomplete for reliable invocation.

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 single 'days' parameter, but the description claims the tool serves 'any of 3,222 US counties' while the schema provides no county parameter or explanation of how the county is selected. This is a significant semantic gap that could prevent correct invocation.

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 power-grid robustness score for US counties, listing the composite factors and output fields. This is a specific verb+resource description that distinguishes the tool from unnamed siblings.

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 provides explicit call scenarios: questions about grid reliability, blackout risk, local power resilience, and specific planning activities. It does not mention when not to use the tool or alternative tools, so it stops short of a 5.

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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