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AlpineDataWorks Intelligence Server

by com.alpinedataworks

JavaScript Ecosystem Activity Index

adw.adw_593
Read-only

Returns a 0-100 JavaScript/npm ecosystem momentum score (weekly downloads of 8 bellwether packages — react, express, lodash, axios, typescript, next, vue, webpack — vs 3 prior weekday-aligned weeks; 50 = flat) with per-package volumes and WoW changes, total weekly downloads, top_package, and fastest_grower. Call when the user asks whether the JS/npm ecosystem is growing or cooling or which packages are moving, or when timing devtools launches or developer-marketing spend. Python: ADW-588. 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

A4.5/5.0
Behavior5/5

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

Annotations already indicate readOnlyHint=true, and the description adds substantial behavioral detail: the score is based on 8 named packages, compared to 3 prior weekday-aligned weeks, with 50 meaning flat, and discloses update cadence ('Updates: daily'). It also specifies returned aggregates (per-package volumes, WoW changes, total, top_package, fastest_grower).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but not bloated; the first sentence packs the score definition, package list, comparison basis, and output fields into one long sentence. It could be split for readability, but it front-loads the core output and every clause adds value.

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?

With no output schema, the description explains the main return values (score, per-package volumes/WoW, totals, top/grower) and clarifies the metric's meaning and use cases. It does not specify the exact JSON response shape, and the history mode return structure is left to the schema, so a small gap remains.

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 single 'days' parameter is fully documented in the input schema (history series, max 1825 days, Gold tier requirement, fallback to snapshot), so schema coverage is 100%. The description does not add parameter-specific semantics, so 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?

Description begins 'Returns a 0-100 JavaScript/npm ecosystem momentum score' specifying verb, resource (JS/npm ecosystem), and exact metric. It distinguishes from the Python equivalent by explicitly noting 'Python: ADW-588'.

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?

States explicit triggers: 'Call when the user asks whether the JS/npm ecosystem is growing or cooling or which packages are moving, or when timing devtools launches or developer-marketing spend.' It also names the Python alternative (ADW-588), providing when/alternative 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