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aviation-weather-mcp-server

by io.github.cyanheads

Get METAR Weather Observations

aviation_get_metar
Read-onlyIdempotent

Get current weather observations (METARs) for one or more airports. Returns decoded fields — wind direction/speed/gusts, visibility, ceiling with its kind (measured, or indefinite for vertical visibility into an obscuration), present weather, temperature, dewpoint, altimeter, cloud layers — plus the computed flight category (VFR/MVFR/IFR/LIFR) and the raw METAR string. Accepts 1–10 ICAO station IDs (e.g., KSEA, KJFK). Use aviation_find_stations to resolve or verify an ICAO ID, or to discover nearby stations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hoursNoHours of observation history to return (1–12). Default 1 returns only the most recent observation per station.
station_idsYesICAO station IDs to query. 1–10 stations per call.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoPresent when the call failed. Absent on success.
noticeNoRecovery guidance naming the missing station IDs. Present only on a partial result. It lists the candidate causes without asserting one — upstream omits the row either way.
missingNoRequested station IDs absent from the result. Absent when none are missing.
partialNoTrue when a requested station produced no observation. False affirms the result covers every requested station, so full coverage is distinguishable from a short batch rather than being inferred from the count.
returnedNoDistinct station IDs that produced at least one observation. Counted per station, not per row — with hours > 1 a station reporting six times still appears once.
requestedNoStation IDs this call asked for, in the order given.
observationsNoWeather observations, one per station/time pair. Multiple entries per station when hours > 1.

TDQS

A3.9/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare the tool as read-only and idempotent, and the description adds context about the output fields (wind, visibility, ceiling, flight category). However, since an output schema is present, the listing of return fields largely duplicates structured data. The description does not disclose additional behavioral traits such as error handling or station lookup behavior beyond the schema, so the value added beyond annotations is modest.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is composed of four sentences, each with a distinct role: purpose, output, input constraints, and usage guidance. It is front-loaded with the core purpose and then provides necessary detail without excessive verbosity, though some repetition of schema-bound information could be trimmed.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (two parameters, an output schema, and read-only annotations), the description covers purpose, output shape, input constraints, and an alternative for station resolution. It does not discuss error cases or rate limits, but these are less critical for a safety-neutral read operation, making the description adequately complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema provides 100% coverage for both parameters, including descriptions, types, defaults, and constraints. The description only repeats examples and limits already in the schema (e.g., '1–10 ICAO station IDs'), adding no new semantic meaning beyond what the schema already offers.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Get current weather observations (METARs)') and the resource ('for one or more airports'), with a specific list of returned fields. This makes it easily distinguishable from sibling tools like TAF forecasts or PIREPs, though it does not explicitly contrast them, the METAR focus is unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly directs users to 'Use aviation_find_stations to resolve or verify an ICAO ID, or to discover nearby stations,' which is a clear alternative for a specific scenario. However, it does not mention when to choose this over aviation_get_taf or aviation_get_pireps, so the guidance is partial rather than comprehensive.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.3/5.0
Disambiguation5/5

Each tool targets a distinct resource type: stations, advisories, METARs, PIREPs, and TAFs. There is no overlap in purpose; an agent can unambiguously choose the right tool for a specific weather data need.

Naming Consistency5/5

All tools follow a consistent `aviation_` prefix with a predictable verb-noun pattern (`find_stations`, `get_metar`, etc.). The naming convention is uniform and immediately communicates the tool's function.

Tool Count5/5

With 5 tools, the server is well-scoped for aviation weather retrieval. Each tool covers a core data type without redundancy, and the count is within the ideal range for a focused MCP server.

Completeness4/5

The set covers primary aviation weather products (METAR, TAF, PIREP, SIGMET) and station lookup. Minor gaps exist such as AIRMET (explicitly rejected) and lack of other products like winds aloft, but the core functionality is coherent and usable without dead ends.

Resources