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AlpineDataWorks Intelligence Server

Sector Money-Flow Rotation Index

adw.adw_548
Read-only

Returns a 0-100 sector-rotation intensity score (daily aggregation of per-stock MFI/CMF money-flow composites across US equity sectors) with trend (rotating vs broad inflow/outflow), per-sector means, and top-5 inflow/outflow leaderboards. Call when the user asks which sectors capital is rotating into or out of, about sector money flow or accumulation/distribution, or when timing sector-ETF tilts, rotation trades, or rebalance decisions. 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

A4.3/5.0
Behavior4/5

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

Annotations already set readOnlyHint=true, and the description aligns by describing a data-return operation. It adds meaningful behavior: daily aggregation of per-stock MFI/CMF composites, update frequency, and the nature of the trend output. It does not over-explain known read-only safety, but gives useful computational context beyond the annotation.

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 tight sentences plus 'Updates: daily.' Each sentence earns its place: the first explains output components, the second gives usage triggers. No redundant wording or over-specification.

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 having no output schema, the description enumerates all expected return elements (score, trend, per-sector means, leaderboards). The only parameter is well-covered by the schema, and the use cases are clear. The tool is simple enough that this fully equips an agent to select and invoke it 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 single optional parameter 'days' is fully documented in the input schema (coverage 100%), including its purpose (historical series), constraints (1-1825), and tier requirement. The main description adds nothing about parameters, so the baseline of 3 applies because the schema does the heavy lifting.

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 begins with a specific verb ('Returns') and clearly defines the resource (sector-rotation intensity score) with a concrete output structure (0-100 score, trend, per-sector means, top-5 leaderboards). It is highly specific and distinguishes from the large sibling set by focusing on sector money-flow rotation, a unique niche.

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?

Explicit 'Call when' clause lists three precise scenarios: asking about sector rotation, money flow/accumulation-distribution, and timing sector-ETF tilts/rebalances. This gives clear triggers, but does not mention when not to use or name alternative tools, so it misses the top criterion for a 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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