US weather travel-risk verdict — Judgment call for a US coordinate and date (within 7 days): LOW/MODERATE/HIGH/SEVERE weather risk with reasons, built from NWS forecast plus active alerts. JSON. Price: $0.015 USDC (Base, via x402).
| Name | Required | Description | Default |
|---|---|---|---|
| lat | Yes | latitude | |
| lon | Yes | longitude | |
| date | No | YYYY-MM-DD within 7 days (default today) |
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 mark it read-only and non-destructive, and the description adds meaningful context: it is a judgment call, sourced from NWS forecast and active alerts, returns severity with reasons, outputs JSON, and costs $0.015 USDC via x402. It also discloses the 7-day date horizon, which is important for invocation expectations.
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?
One dense sentence contains the purpose, inputs, output levels, data sources, output format, and pricing — all front-loaded with no filler. Every clause earns its place.
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?
The description fully prepares an agent to select and call the tool: it explains what the tool returns, what it consumes, how it derives the verdict, and even the cost. With no output schema, the stated severity levels and 'with reasons' adequately describe the return shape.
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 coverage is 100%, with lat, lon, and date already documented. The description reinforces that coordinates are US-based and dates must be within 7 days, but adds little beyond the schema's existing parameter descriptions.
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 identifies the tool as producing a US weather travel-risk verdict with explicit severity levels (LOW/MODERATE/HIGH/SEVERE) and reasons, built from NWS forecast plus active alerts. This differentiates it from sibling weather tools like weather_forecast or weather_alerts by emphasizing a synthesized judgment rather than raw data.
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 gives clear context: use this when you need a travel-risk judgment for a US coordinate and date, not just raw weather data. It does not explicitly name alternative tools or exclusion conditions, but the 'verdict with reasons' framing strongly implies the intended use case over siblings.
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.