Aviation weather METAR/TAF (NOAA) — Raw and decoded METAR (and optional TAF) for any airport ICAO code. Source: NOAA Aviation Weather Center. JSON. Price: $0.003 USDC (Base, via x402).
| Name | Required | Description | Default |
|---|---|---|---|
| taf | Yes | true to include TAF | |
| station | Yes | ICAO code e.g. KBNA |
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?
With readOnlyHint and destructiveHint annotations, the safety profile is already covered; the description adds meaningful behavioral context, including raw and decoded output, optional TAF inclusion, JSON format, NOAA source, and a specific price. This goes beyond what annotations and schema convey.
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 compact and front-loaded with the core capability, followed by useful operational details (source, format, price). It repeats the NOAA title phrase, but the additional clauses are all relevant and take little space.
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 two-parameter read-only tool with no output schema, the description covers input scope, optional TAF behavior, output format, and pricing. It does not describe detailed return structure or error behavior, but 'raw and decoded METAR' and 'JSON' give sufficient context for selection.
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%, and both parameters already have descriptions (station example, taf 'true to include TAF'). The description confirms 'optional TAF' but does not add new parameter meaning beyond the schema, so the baseline 3 applies.
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 identifies METAR/TAF as a specific aviation weather resource and narrows the scope to any airport ICAO code, which distinguishes it from weather_current, weather_forecast, and other weather siblings. It lacks an explicit verb like 'retrieves' or 'returns,' but 'Raw and decoded METAR... for any airport ICAO code' makes the function clear.
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 use case is implied: call this when raw or decoded METAR/TAF aviation weather for an ICAO station is needed. There is no explicit guidance about when to prefer this over weather_current, weather_forecast, or other weather siblings, and no exclusions are stated.
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.