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AlpineDataWorks Intelligence Server

by com.alpinedataworks

Supply-Chain Modal Shift

adw.adw_394
Read-only

Returns a 0-100 supply-chain modal-shift score (rail-to-truck cargo rebalancing inferred from AIS-observed freight flows, time series to 2001) with truck_share_delta, shift_velocity, disruption_flag, confidence, and methodology_version. Call when the user asks about rail strikes, trucking capacity, freight disruption, or local inflation risk, or when timing freight-rate locks, inventory pre-buys, or regional price 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

A4.5/5.0
Behavior5/5

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

Beyond the readOnlyHint annotation, the description adds rich behavioral context: score interpretation, AIS-observed methodology, time series to 2001, monthly update cadence, and output field enumeration. It accurately complements the annotations without 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 two information-dense sentences plus a short 'Updates: monthly' note. Every clause adds value, and the front-loading of the return value and fields aids quick scanning.

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?

Despite lacking an output schema, the description enumerates return fields and score range, providing sufficient context for interpreting results. The optional history parameter is fully described in the schema, and the description covers data source, update frequency, and use cases, making it complete for an agent to select and invoke correctly.

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 schema fully documents the only parameter 'days' with range, meaning, and Gold-tier requirement (100% coverage). The description adds no additional parameter-specific details, which is acceptable given the schema coverage baseline of 3.

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 supply-chain modal-shift score and lists the specific output fields and methodology. It uses a specific verb ('Returns') with a well-defined resource and distinguishes itself via concrete use cases like rail strikes and freight disruption.

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 explicitly enumerates when to call the tool ('Call when...') covering rail strikes, trucking capacity, freight disruption, inflation risk, and freight timing decisions. However, it does not mention alternative tools or provide 'when not to use' guidance, so it misses the full 5.

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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