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AlpineDataWorks Intelligence Server

by com.alpinedataworks

Agricultural Input/Climate Stress (Corn Belt)

adw.adw_213
Read-only

Returns a 0-100 US Corn Belt precipitation/soil-moisture stress score (weekly NASA POWER agroclimatology since 1981; 0.55precip-deficit-vs-30d + 0.45root-moisture-deficit) with stress_score, precip_deficit, root_moisture_deficit. Call when the user asks about Corn Belt drought, corn crop stress, soil moisture, rainfall deficits, ethanol or livestock feed costs, or when timing corn/ethanol futures positioning ahead of weekly USDA crop progress releases. Updates: weekly.

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

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, so the description's disclosure burden is lower. It adds meaningful behavioral context: weekly NASA POWER data since 1981, the weighting formula, and output fields. It does not overstate side effects and is consistent 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?

The description is three sentences: purpose, usage triggers, and update cadence. It is dense with information yet word-efficient and front-loaded.

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?

For a read-only data tool with one optional parameter, the description covers output fields, methodology, data source, and update frequency. However, it does not explicitly state that higher scores indicate more severe stress, and the weekly/daily distinction between snapshot and history could be clearer.

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?

Schema coverage is 100% and the days parameter already has a detailed description covering history, Gold tier, and fallback. The main description adds no parameter-specific meaning, but the formula clarifies the score semantics. Baseline 3 is appropriate given the schema's thoroughness.

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 opens with 'Returns a 0-100 US Corn Belt precipitation/soil-moisture stress score', specifying the exact verb, resource, and scope. The formula and output field names further delineate the tool's function from the many adw siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly instructs 'Call when the user asks about Corn Belt drought, corn crop stress, soil moisture, rainfall deficits, ethanol or livestock feed costs...' This provides clear, actionable usage context. It also mentions timing relative to USDA releases, which is valuable guidance.

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