adw.obesity_riskAge-adjusted adult obesity prevalence index for any US ZIP, city, or county (CDC PLACES 2025). Returns national percentile rank, band, and raw prevalence. Pass entity as a 5-digit ZIP, 'City, ST', or 'County Name, ST'.
| Name | Required | Description | Default |
|---|---|---|---|
| entity | Yes | 5-digit ZIP/ZCTA (e.g. '33935'), 'City, ST' (e.g. 'Fort Myers, FL'), or 'County Name, ST' (e.g. 'Lee County, FL'). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, and the description adds useful behavioral details beyond that: it explicitly states what the tool returns (national percentile rank, band, raw prevalence), mentions the data is age-adjusted, and cites the CDC PLACES 2025 source. This enriches the agent's understanding without contradicting the 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?
The description is exactly two sentences. The first sentence front-loads the purpose and data source, while the second gives necessary input formatting. Every word earns its place with no redundancy or fluff.
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 simple one-parameter read-only tool, the description is complete: it explains the input format, the geographic scope, the data source, and the return values. No output schema exists, but the description covers what the agent can expect to receive, making it fully adequate for correct invocation.
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 input schema already provides 100% coverage of the only parameter `entity`, including examples for ZIP, city, and county formats. The description repeats this format guidance without adding additional meaning, so the baseline of 3 is appropriate when the schema does the heavy lifting.
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 a specific verb+resource: it returns an age-adjusted adult obesity prevalence index for any US ZIP, city, or county. It distinguishes itself from sibling tools by focusing on obesity and specifying the CDC PLACES 2025 data source, plus the exact outputs (percentile rank, band, raw prevalence).
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 provides clear context for when to use the tool: to get obesity prevalence data for a geographic entity. It does not explicitly mention alternatives or exclusion criteria, but the scope is unambiguous and the input format is clearly illustrated. This qualifies as clear context with no exclusions.
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.