adw.health_riskGeographic health-risk index for any US ZIP/ZCTA (CDC PLACES, 40 measures). Returns a 0-100 risk score, band, confidence, and the top risk drivers. Pass zip.
| Name | Required | Description | Default |
|---|---|---|---|
| zip | Yes | 5-digit US ZIP / ZCTA, e.g. '10001'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the description doesn't need to reiterate safety. It adds meaningful context by explaining the return payload (score, band, confidence, top drivers) and the data source/scope, which goes beyond the annotations. However, it doesn't disclose potential edge cases like invalid ZIP formats or unavailable data.
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?
The description is two sentences, front-loaded with the core purpose, and includes essential details (data source, output fields, input parameter) without any waste. It is well-structured and immediately scannable.
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 single-parameter, read-only tool with no output schema, the description adequately covers purpose, input scope, and return values. It is complete enough for basic use, though it could mention what happens for invalid or non-US ZIPs, but that's not critical given the simplicity.
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?
Schema description coverage is 100% for the single `zip` parameter, so the schema already documents the format and example. The description adds 'any US ZIP/ZCTA' scope, which is a mild semantic flourish but doesn't significantly enhance parameter understanding beyond the schema.
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 computes a geographic health-risk index for US ZIP/ZCTA codes, citing the CDC PLACES data source and 40 measures. It also specifies the output (0-100 risk score, band, confidence, top drivers), making the purpose specific and distinguishable from sibling tools that focus on county-level or specific risk types.
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?
The description implies usage when a ZIP-level health-risk index is needed, but it does not explicitly contrast with alternative tools like adw.county_cancer, adw.obesity_risk, or adw.air_quality_risk. There's no when-not-to-use guidance, so the agent must infer the appropriate context from the description and sibling names.
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.