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AlpineDataWorks Intelligence Server

Software Dependency Risk

adw.adw_436
Read-only

Returns a 0-100 software dependency risk score for a basket of PHP/Composer packages (release-frequency and download-volume z-scores against 13 years of Packagist baselines, refreshed daily) with risk_level, basket_z, stalest_package, release_window_days, and packages_analyzed. Call when the user asks about package health, abandonware risk, Composer dependency hygiene, or supply-chain exposure, or when timing a dependency migration, framework upgrade, or maintenance sprint. 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

A3.8/5.0
Behavior3/5

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

The description adds valuable context about refresh cadence ('refreshed daily') and statistical baselines beyond the readOnlyHint annotation. However, it refers to a 'basket of PHP/Composer packages' without explaining how that basket is provided, since the only parameter is `days`. This is a significant transparency gap.

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 front-loaded with the core purpose, followed by concrete usage triggers and an update-frequency note. It is dense but not wasteful, with every sentence contributing useful information.

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

Completeness3/5

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

The description covers the output fields and usage scenarios, and the schema covers the parameter. Yet it leaves the basket specification unexplained, which is a notable omission for a tool with no package input. Error conditions and fallback behavior are also absent.

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 sole parameter `days` is fully described in the schema, including its optionality and the Gold tier requirement. The description adds no parameter-specific information, so with 100% schema coverage the 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 clearly states the tool returns a 0-100 risk score for PHP/Composer packages, specifying the methodology (z-scores against 13 years of Packagist baselines) and the exact output fields. This distinguishes it from other risk-related tools by domain and functionality.

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 when to call the tool (package health, abandonware risk, Composer dependency hygiene, supply-chain exposure, migration timing). However, it does not mention alternatives or when-not-to-use, so it misses the top tier.

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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