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Glama

AlpineDataWorks Intelligence Server

by com.alpinedataworks

US Commercial Insurance Renewal Spike

adw.adw_401
Read-only

Returns a 0-100 US commercial insurance renewal spike score (year-over-year premium change computed from carrier renewal quote aggregates, 27 years of history) with premium_yoy_pct, spike_flag, trend_direction, history_percentile, confidence, and methodology_version. Call when the user asks about commercial insurance renewal costs, hard-market pricing, or office operating-expense pressure, or when timing Class B/C office underwriting, NOI budgets, or hold/sell decisions. 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

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, but the description adds valuable context beyond that: 27 years of history, update frequency (monthly), and the methodology behind the score. No contradictions with annotations.

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?

Three sentences, each packed with useful information: what is returned, when to use it, and update frequency. No filler or redundancy; the structure is efficient and well organized.

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?

For a simple read-only metric tool with one optional parameter and no output schema, the description covers all key aspects: output fields, use cases, update cadence, and data lineage. It gives the agent enough to select and invoke the tool 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?

Schema coverage is 100% and the 'days' parameter has a detailed description including range, purpose, and tier requirement. The tool description adds no extra parameter meaning, which is acceptable given the schema already carries the information.

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 opens with 'Returns a 0-100 US commercial insurance renewal spike score' and enumerates the exact output fields, making the tool's purpose unmistakable. It clearly differentiates from the opaque sibling names by naming a specific metric and scope (US commercial insurance).

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 trigger conditions are provided ('Call when the user asks about...'), covering multiple relevant scenarios. However, it does not mention when not to use the tool or name alternative tools, so it falls just short of 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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