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AlpineDataWorks Intelligence Server

by com.alpinedataworks

AI/Software Ecosystem Health Driver

adw.adw_008
Read-only

Returns a 0-100 AI/software ecosystem health score (daily, reproducible from keyless GitHub REST data: 10-repo AI/ML basket, 0.6 stars-mindshare + 0.4 issue/star engagement) with health_score, primary_driver_label, organic_vs_hype, and basket_total_stars. Call when the user asks whether AI open-source momentum is organic growth or hype, about developer mindshare or ecosystem sentiment, or when timing AI investment-thesis reassessments, venture portfolio reviews, or tech-stack bets. 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

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, and the description adds that data is daily, reproducible, and uses keyless GitHub REST data, implying no auth. It also discloses the methodology and update frequency. However, it doesn't describe behavior for the 'days' parameter beyond schema, but that's covered there.

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 efficient; however, 'Updates: daily' is redundant with the earlier '(daily...' mention. Otherwise, every sentence serves a purpose.

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?

The description names all four return fields, explains the scoring formula, and covers the key use cases. With no output schema, this is sufficient to understand the tool's behavior. It could mention the history parameter but the schema covers it, so no gap.

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 only parameter 'days' has a full schema description including optionality, range, and the Gold tier limitation. The description itself adds no additional parameter detail, so the baseline of 3 is appropriate given 100% schema coverage.

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 clearly states it returns a 0-100 health score with a specific formula and defines the returned fields. It is specific about the resource (AI/software ecosystem) and distinguishes from generic siblings by naming the unique score and drivers.

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?

Explicitly provides multiple when-to-call scenarios, including organic vs hype, mindshare, and investment timing. It doesn't explicitly say when not to use it, but the positive guidance is strong and clear.

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