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AlpineDataWorks Intelligence Server

Healthcare Cost Transparency Gap

adw.adw_312
Read-only

Returns a 0-100 pharma/device industry physician-payment intensity score (annual CMS Open Payments record counts vs 3-year baseline mean, since 1950) with trend, top_drivers by payment category (consulting, research, education), and source_lineage. Call when the user asks about drug or device company payments to US doctors, physician financial ties, conflicts of interest, Sunshine Act, or Open Payments trends, or when timing healthcare compliance reviews of industry engagement exposure. Updates: annually.

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

A4/5.0
Behavior4/5

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

The description adds context beyond the readOnlyHint annotation: it specifies the data source (CMS Open Payments), score range, components, update frequency, and the historical baseline. It does not fully explain return format or limitations, but it provides substantial behavioral context.

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 concise and front-loaded, stating the core output first followed by usage triggers and update frequency. Every sentence adds value, with no filler or repetition.

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?

For a read-only tool with one optional parameter and no output schema, the description provides a solid summary of what is returned, when to use it, and data freshness. It could be improved by clarifying the interpretation of the score, but the key aspects are covered.

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?

Input schema coverage is 100% for the single optional 'days' parameter, which is already described in the schema. The main description does not mention the parameter or add additional meaning, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

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 physician-payment intensity score with trend, top drivers, and source lineage, which is a specific verb+resource. It does not explicitly differentiate from siblings (e.g., by naming alternative tools), but the topic is distinct and unambiguous.

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 provides explicit trigger phrases ('Call when the user asks about drug or device company payments... Sunshine Act...') and timing use cases. It does not mention when not to use or alternatives, but the guidance is clear and actionable.

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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