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AlpineDataWorks Intelligence Server

by com.alpinedataworks

Solar Flare Activity Index

adw.adw_563
Read-only

Returns a 0-100 solar X-ray flare activity index (hourly, keyless; NOAA SWPC GOES 0.1-0.8nm flux plus strongest 24h flare on a log-flux ramp — B11, C38, M65, X92+) with flux_class, recent_flares, and the NOAA R-scale radio-blackout level. Call when the user asks about solar flares, space weather, HF radio blackouts, or GNSS degradation, or when timing polar flight dispatch, maritime HF communications, or RTK-precision survey work. Updates: hourly.

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

A4.5/5.0
Behavior5/5

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

Annotations declare readOnlyHint=true, and the description adds behavioral detail: 'keyless', 'hourly' updates, calculation method ('log-flux ramp'), and output components. This context goes beyond the annotation to explain what the tool returns and how it behaves.

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 information-dense, with the main output in the first clause and use cases in a single sentence. No filler; every clause adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the one optional parameter, readOnly annotation, and no output schema, the description covers return values, use cases, update frequency, and parameter behavior (via schema). It fully equips the agent to select and invoke the 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?

Schema coverage is 100%; the only parameter 'days' has a detailed description explaining the history series and Gold tier requirement. The tool description adds no additional parameter semantics, so baseline 3 applies.

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 solar X-ray flare activity index' and enumerates specific outputs (flux_class, recent_flares, NOAA R-scale), making the tool's purpose unmistakable. It also specifies the data source (NOAA SWPC GOES) and the index scale, which distinguishes it from generic data 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?

It explicitly lists trigger conditions: 'Call when the user asks about solar flares, space weather, HF radio blackouts, or GNSS degradation, or when timing polar flight dispatch...' This gives clear when-to-use guidance. It lacks explicit exclusions or alternative tool references, so not 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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