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AlpineDataWorks Intelligence Server

Global River Flood Risk Forecast

adw.adw_579
Read-only

Returns a 0-100 river-flood risk forecast (7-day GloFAS peak-discharge vs climatological norms at 12 major world rivers — Mississippi, Amazon, Ganges, Yangtze, Mekong, Rhine, more) with flood_level, rivers_elevated, max_discharge_ratio, highest_risk_river, and a riskiest-first per-river table. Call when the user asks about flooding, river levels, or discharge anomalies, or when timing barge logistics, commodity-shipping, or disaster-response decisions. Updates: daily.

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

A4.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, covering the safety profile. The description adds 'Updates: daily' and the GloFAS data source, but does not disclose limitations or other behavioral caveats. The Gold tier requirement for historical data is in the schema, not the main description. This adds some value but not rich context, so 3.

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?

Two sentences, no filler. The first sentence front-loads the core action and output details; the second provides use cases and update frequency. Every clause earns its place, and the structure is easy to scan.

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?

For a simple read-only forecast tool with one optional parameter and no output schema, the description covers purpose, river scope, output fields, use cases, and update frequency. The schema thoroughly documents the parameter, and annotations confirm read-only behavior. No significant information is missing.

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% for the single 'days' parameter, including the Gold tier behavior, so the schema fully documents the parameter. The description does not add any parameter-specific semantics beyond what is already in the schema, hence the baseline 3.

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 river-flood risk forecast' and specifies the exact rivers and output fields (flood_level, rivers_elevated, etc.), giving a clear verb+resource+scope. This distinguishes it from any sibling tool, as none are described as flood-specific.

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 'Call when the user asks about flooding, river levels, or discharge anomalies, or when timing barge logistics, commodity-shipping, or disaster-response decisions.' This provides clear context for when to use the tool, though it does not mention alternatives or exclusions, so a 4 rather than 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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