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AlpineDataWorks Intelligence Server

by com.alpinedataworks

Pharma Patent Exclusivity Velocity

adw.adw_386
Read-only

Returns a 0-100 pharma patent-exclusivity velocity score (speed of first-generic entry after patent and exclusivity expiry, computed from FDA Orange Book approval records) with median_days_to_first_generic, anda_approvals_trailing_12m, velocity_trend, and methodology_version. Call when the user asks about generic drug entry, patent cliffs, branded revenue erosion, or formulary savings, or when timing hospital drug-purchasing contracts and pharma revenue forecasts. Updates: monthly.

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?

Annotations already declare readOnlyHint=true and openWorldHint=false; description adds meaningful context: data source (FDA Orange Book), computation basis (first-generic entry), update frequency (monthly), and return components. No contradiction.

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 compact and well-structured: what it returns, the return fields, when to use it, and update frequency. Every sentence earns its place with no fluff.

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 one optional parameter and read-only annotation, the description gives sufficient operational details: data source, use cases, and return field names. It doesn't explain each field's meaning or output format, but the description is adequate for a simple read-only metric.

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 schema with details about optionality, history series, Gold tier requirement, and fallback to snapshot. The main description adds no additional parameter behavior, so baseline 3 is justified.

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 states a specific verb and resource: 'Returns a 0-100 pharma patent-exclusivity velocity score' and lists the return fields. This is uniquely identifiable and distinguishes from the many sibling tools by domain and computed metric.

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?

Provides explicit 'Call when' scenarios (generic drug entry, patent cliffs, etc.) and mentions update cadence. However, it does not state when not to use it or explicitly name alternative tools, so it falls one point short of perfect 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