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AlpineDataWorks Intelligence Server

Global Disaster Activity Index

adw.adw_559
Read-only

Returns a live hourly 0-100 index of worldwide natural-disaster activity (GDACS EC-JRC/UN OCHA events weighted Red=3, Orange=2, Green=1) with counts by event type and alert level plus top Red/Orange events. Call when the user asks how much disaster activity is happening globally or which earthquakes, cyclones, floods, volcanoes, droughts, or wildfires are most severe right now, or when timing logistics reroutes, continuity escalations, or humanitarian response. 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

A4.2/5.0
Behavior4/5

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

The annotations already declare readOnlyHint=true, so the description adds value beyond that by explaining the data source, the weighting scheme (Red=3, Orange=2, Green=1), the content of the output (counts by type/alert, top events), and the hourly update cadence. It does not go into pagination or exact return structure, but the extra context is meaningful.

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 dense sentences, front-loaded with the primary function, followed by concrete use cases and an update note. Every sentence earns its place without fluff, making it efficient and scannable.

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?

Even without an output schema, the description explains what the tool returns (index, counts by type/alert, top Red/Orange events), its data source, update frequency, and representative use cases. It could detail the exact structure of the return object, but for a read-only data lookup tool, this is sufficient.

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 description coverage is 100% because the only parameter 'days' has a thorough description in the schema. The main description does not add parameter-specific details beyond the schema; it only implies a 'live' snapshot, while the schema covers the history option. Thus, baseline 3 is appropriate.

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 begins with a specific, actionable verb ('Returns a live hourly 0-100 index') and clearly defines both the resource (worldwide natural-disaster activity) and the scope (GDACS EC-JRC/UN OCHA events weighted by alert level). It also lists the event types covered, distinguishing it from generic 'disaster' tools and aligning with the title.

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 explicitly states when to invoke the tool ('Call when the user asks how much disaster activity is happening globally... or when timing logistics reroutes, continuity escalations, or humanitarian response'), providing clear context. However, it does not name alternative tools or explicitly state when not to use it, which prevents a perfect 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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