adw.sampleFREE sample — no API key or credits required. Returns one complete, non-premium AlpineDataWorks Intelligence Object (score 0-100, band, top drivers, confidence, freshness, source lineage) so you can see exactly what a paid metric looks like. Call this first to try the data before subscribing.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations establish read-only safety. The description adds valuable behavioral context: no API key/credits, returns exactly one complete object with a specific list of attributes (score, band, top drivers, confidence, freshness, source lineage), and is a non-premium sample. This goes beyond the structured annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, each serving a distinct purpose: free access, payload description, and when to use. Front-loaded with the key differentiator. No wasteful language.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter tool with annotations and no output schema, the description fully explains what the tool does, what it returns, and when to use it. Minor missing details like whether repeated calls return the same sample are not critical for the intended evaluation use case.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and schema coverage is 100%, so there is nothing to document. The description appropriately focuses on return value and usage, which aligns with the baseline for no-parameter tools.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns a free sample Intelligence Object, with specific fields listed. It differentiates itself from the many adw.adw_XXX metric siblings by stressing 'no API key or credits required' and positioning itself as a preview of what paid metrics look like.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance: 'Call this first to try the data before subscribing.' This tells the agent to use the sample before engaging with paid tools. It doesn't name specific alternatives, but the context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.