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AlpineDataWorks Intelligence Server

Streamflow Stress Index

adw.adw_534
Read-only

Returns a 0-100 county-level streamflow stress score (current gauge-measured river discharge vs. historical flow normals) with stress_score, flow_percentile, severity_band, station_count, as_of, and methodology_version fields. Call when the user asks about drought severity, water stress, low river levels, or irrigation and water-supply risk in a county or region, or when timing water-dependent decisions — crop lending, facility siting, drought-contingency triggers. 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 the description doesn't need to restate safety. It adds useful context about the score's definition (current vs. historical normals) and notes the update cadence ('Updates: on source cadence'), providing freshness expectations beyond what annotations convey.

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 compact and well-structured: it states what it returns, when to call, and update frequency in three logical sentences. Every sentence provides necessary information with no 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 lists return fields and explains the score's meaning, but it never explains how the target county or region is specified, especially since the input schema only has an optional 'days' parameter. The agent is left guessing whether the county comes from conversation context, a default, or some implicit state, which is a critical omission for a county-level tool.

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 thoroughly described in the schema (100% coverage), so the description doesn't need to add more. However, the description's county-level claim is unsupported by any location parameter, and the description fails to clarify how the county is selected, which is a semantic gap that affects invocation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

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-level streamflow stress score based on current gauge-measured discharge vs. historical normals, and enumerates the exact fields returned. However, it does not distinguish this tool from any of the many sibling tools, missing the 'distinguishes from siblings' criterion for a 5.

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 strong when-to-use guidance by citing specific user intents (drought severity, water stress, low river levels, water-supply risk) and even decision contexts (crop lending, facility siting). It lacks mention of alternatives or when not to use, so it cannot earn 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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