US hourly forecast (NWS) — Hour-by-hour forecast for any US coordinate: temperature, precipitation chance, wind, conditions for the next 48 hours. Source: National Weather Service. JSON. Price: $0.003 USDC (Base, via x402).
| Name | Required | Description | Default |
|---|---|---|---|
| lat | Yes | latitude | |
| lon | Yes | longitude | |
| hours | No | 1-48 (default 24) |
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false. The description adds useful behavioral context beyond those: the National Weather Service as source, JSON output format, the 48-hour window, and the $0.003 pricing. This is enough context for a read-only weather lookup, though it omits details like unit conventions or coordinate format.
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 one dense, front-loaded sentence followed by short factual tags (source, format, price). Every phrase adds information, and there is no filler or repetition of schema content.
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 read-only, two-required-parameter tool, the description covers the data available, the geographic scope, the time range, source, and response format. It lacks a few details such as units or timezone behavior, but the missing information is unlikely to prevent 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?
Schema description coverage is 100%, so the schema already documents lat, lon, and hours. The description adds only minor semantics—'US coordinate' for lat/lon and 'next 48 hours' aligning with the hours parameter. That is helpful but does not substantially extend what the schema already provides.
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 is precise: 'US hourly forecast (NWS)' plus 'Hour-by-hour forecast for any US coordinate: temperature, precipitation chance, wind, conditions for the next 48 hours.' It names the resource, the data fields, and the horizon, and it is clearly distinguishable from weather_current, weather_forecast, weather_metar, and weather_alerts.
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 makes the intended use case obvious: when an agent needs an hour-by-hour weather forecast for a US location. It also implies the non-US exclusion via 'US coordinate.' However, it does not explicitly point to sibling alternatives such as weather_current or weather_forecast, so it stops short of perfect routing guidance.
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.
Many tools are clearly separated by prefix and data source, but several bundled products overlap heavily: vehicle_deal_check vs vehicle_report, realestate_property_report vs realestate_site_risk, finance_company_360 vs finance_health_scan, and domain_due_diligence vs email_domain_check/business_vet. An agent would frequently struggle to pick the correct premium bundle.
Tool names overwhelmingly follow a consistent snake_case category-prefix pattern like weather_, crypto_, vehicle_, finance_, and geo_. Minor deviations such as bare names (domain, ip) and noun-verb forms (dns_lookup, url_check) are easy to learn and don't create real confusion.
50 tools is far beyond the typical well-scoped 3–15 range and will require heavy filtering to navigate. The broad multi-domain data marketplace partially justifies the size, but it would be more coherent split into per-domain servers or consolidated further.
For a read-only data/diligence marketplace, the surface is quite comprehensive: weather, vehicle, crypto, SEC/finance, domain/email, sanctions, and geo workflows all have core operations plus fused verdict bundles. Minor gaps exist—such as a simple crypto price lookup or vehicle market value—but agents can usually work around them.