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Tanod skypeek METAR/TAF Decoder
x402
Tanod skypeek METAR/TAF Decoder is a paid API for AI agents from tanod.dev, paid per call via x402, $0.001/call, status unknown (last checked 2026-10-12).
Decodes a raw METAR or TAF aviation weather report into structured, human-readable JSON fields including wind, clouds, visibility, altimeter, temperature, and flight category.
Price / call$0.001
Success—
StatusUnknown
How this service works
One MCP server and HTTP API with 193 pay-per-call tools for AI agents (security checks, chain data, finance, aviation, documents), plus free uptime and MikroTik monitoring and 130+ free browser tools. No signup; free daily tier. Operated by an AI.
Output
Returns a JSON object with a 'kind' field (metar or taf), a 'decoded' object containing structured fields (station, observed time, wind with speed/direction/gust/unit/calm/variable flags, visibility in meters and statute miles, clouds array with cover type and height, temperature, dewpoint, altimeter in hPa and inHg, trend, flight category, remarks, unparsed tokens), and a disclaimer noting informational-only use.
Example use cases
Pilot pre-flight weather check
“I just pulled this raw METAR for KJFK and I need to know the wind direction and speed, visibility, cloud cover, and whether it's VFR or IFR conditions — can you decode it? METAR KJFK 121551Z 27015KT 10SM FEW250 24/12 A2995 RMK AO2”
Dispatch weather briefing automation
“We received this batch of METAR strings from our ops team and need to parse the altimeter settings, temperatures, and flight categories from each one — start with this one: METAR EGLL 121550Z 25012KT 9000 BKN020 16/10 Q1012”
TAF forecast interpretation
“Can you parse this TAF report and tell me what the expected wind and visibility conditions are and when they change? TAF KSFO 121120Z 1212/1318 28012KT P6SM SKC FM121800 30015G25KT P6SM FEW020”
Technical details
Endpoint
POSThttps://tanod.dev/v1/aviation/metar/decode?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need to programmatically parse and structure raw METAR or TAF strings into machine-readable fields for downstream processing, display, or alerting — especially within AI agent workflows that receive raw aviation weather text. Prefer this over general-purpose LLM parsing when you need consistent structured output (flight category, altimeter values, cloud layers) with precise field names. Note: explicitly not certified for operational flight planning or navigation decisions.
Side effects
- Malformed or empty METAR/TAF string returns a parsing error or unparsed token list
- Input exceeding 2,000 characters is rejected with a validation error
- Ambiguous report type with kind='auto' may misclassify edge-case inputs
- Non-standard or proprietary METAR extensions may appear in the 'unparsed' array rather than decoded fields
- Network or payment authorization failure returns a 402 or 5xx error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| raw | string | One METAR or TAF report as text (at most 2,000 characters). |
| kind | string | Report type; auto detects it. |
Output
Schema · Response
{
"type": "json",
"example": {
"kind": "metar",
"note": "Day / hour / minute are as reported (UTC); the month and year are not part of a METAR or TAF.",
"decoded": {
"type": "METAR",
"wind": {
"calm": false,
"gust": null,
"unit": "KT",
"speed": 5,
"gust_kt": null,
"speed_kt": 5,
"variable": false,
"direction_deg": 100,
"direction_compass": "E"
},
"trend": "NOSIG",
"clouds": [
{
"type": null,
"cover": "FEW",
"height_ft": 1800,
"cover_text": "few"
},
{
"type": null,
"cover": "SCT",
"height_ft": 2500,
"cover_text": "scattered"
}
],
"remarks": null,
"station": "RPLL",
"observed": {
"day": 8,
"hour": 3,
"minute": 0
},
"unparsed": [],
"altimeter": {
"hpa": 1008,
"inhg": 29.77
},
"dewpoint_c": 26,
"visibility": {
"meters": 9999,
"qualifier": "greater_than_or_equal",
"statute_miles": 6.21
},
"temperature_c": 30,
"flight_category": "VFR"
},
"disclaimer": "Informational only. Not for operational use or flight planning."
}
}Tags
x402
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