US weather forecast (NWS) — 7-day forecast for any US coordinate: periods with temperature, wind, precipitation chance, narrative. Source: National Weather Service. JSON. Price: $0.003 USDC (Base, via x402).
| Name | Required | Description | Default |
|---|---|---|---|
| lat | Yes | latitude e.g. 36.16 | |
| lon | Yes | longitude e.g. -86.78 |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish readOnlyHint=true and destructiveHint=false. The description adds useful behavioral context beyond that: the source (NWS), output fields, JSON format, and the $0.003 USDC price. No auth or rate-limit details are provided, but the read-only nature and simple call make this acceptable.
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 extremely compact and front-loaded: purpose, scope, output fields, source, format, and price are all conveyed in one efficient sentence plus short fragments. There is no filler or repetition of schema details.
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-parameter tool with no output schema, the description provides enough to understand what will be returned and what it costs. Units (Fahrenheit/Celsius) are not specified, but the listed output fields and NWS source give sufficient context for most callers.
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?
Input schema covers both required parameters (lat, lon) with explanatory examples, so schema coverage is 100%. The description adds only 'any US coordinate' as extra context, which is helpful but does not substantially extend parameter meaning 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?
Description clearly identifies the tool as a 7-day NWS forecast for any US coordinate, listing key output fields (temperature, wind, precipitation chance, narrative). It is easily distinguishable from siblings like weather_current and weather_hourly by the '7-day forecast' scope, though it does not explicitly name those alternatives.
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 '7-day forecast' phrasing implies use for forecasting rather than current conditions or alerts, and 'US coordinate' defines geographic scope. However, there is no explicit statement of when not to use this tool or which sibling should be chosen for current weather, hourly data, alerts, or other conditions.
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.