Advanced Weather MCP – Locations, Routes, Air Quality & Solar

MCP server providing structured access to global weather, air quality, solar and derived risk signals based on Open-Meteo.

No external API keys required. Recommended memory is 512MB.

Units are metric by default. Set Actor input defaultUnits or per-tool units to imperial. Derived metrics follow the selected unit system. Free plans: only get_point_weather and get_air_quality (forecastDays <= 1, up to 4 hourly fields). Paid plans: all tools available.

What this server provides

This server exposes weather and environmental data in formats that are useful for automation and AI agents:

• Point forecasts (current, hourly, daily)
• Air quality and AQI
• Solar radiation and energy potential
• Route-based weather aggregation
• Area-based weather aggregation
• Derived comfort and safety metrics
• Standardized risk flags

All data is normalized, timezone-aware, and suitable for direct machine consumption.

MCP tools

get_point_weather

Returns weather for a single GPS location.

Provides:

• Temperature, wind, rain, humidity, cloud cover
• Hourly or daily time series
• Derived metrics: heat index, wind chill, dew point

Input example:

{
  "lat": 50.745,
  "lon": 3.6,
  "mode": "hourly",
  "forecastDays": 3,
  "hourlyFields": [
    "temperature_2m",
    "relative_humidity_2m",
    "precipitation_probability",
    "wind_gusts_10m"
  ],
  "timezone": "auto"
}

Output example:

{
  "location": { "lat": 50.745, "lon": 3.6, "timezone": "Europe/Brussels" },
  "units": { "hourly": { "temperature_2m": "C", "wind_gusts_10m": "km/h" } },
  "hourly": [
    {
      "time": "2026-01-13T12:00",
      "temperature_2m": 6.2,
      "relative_humidity_2m": 80,
      "precipitation_probability": 35,
      "wind_gusts_10m": 28,
      "dewPointC": 3.1
    }
  ]
}

get_air_quality

Returns air quality and pollutant levels for a GPS location.

Provides:

• AQI
• PM2.5, PM10, NO2, O3
• Summary values for the next 24 hours and overall period

Input example:

{
  "lat": 50.745,
  "lon": 3.6,
  "forecastDays": 2,
  "hourlyFields": ["aqi", "pm2_5", "pm10", "no2", "o3"],
  "timezone": "auto"
}

Output example:

{
  "location": { "lat": 50.745, "lon": 3.6, "timezone": "Europe/Brussels" },
  "units": { "system": "metric" },
  "hourly": [
    { "time": "2026-01-13T12:00", "aqi": 54, "pm2_5": 9.2, "pm10": 15.1, "no2": 12.4, "o3": 62.0 }
  ],
  "summary": { "maxAqiNext24h": 62, "maxAqiOverall": 72, "category": "moderate" }
}

get_solar_forecast

Returns solar radiation for a GPS location.

Provides:

• Hourly solar radiation metrics
• Daily total energy per m2
• Time windows with highest average radiation

Input example:

{
  "lat": 50.745,
  "lon": 3.6,
  "forecastDays": 2,
  "hourlyFields": ["shortwave_radiation", "direct_normal_irradiance"],
  "timezone": "auto"
}

Output example:

{
  "location": { "lat": 50.745, "lon": 3.6, "timezone": "Europe/Brussels" },
  "units": { "system": "metric" },
  "hourly": [
    { "time": "2026-01-13T12:00", "shortwave_radiation": 210, "direct_normal_irradiance": 340 }
  ],
  "dailySummaries": [
    { "date": "2026-01-13", "totalShortwaveWhM2": 1550, "peakShortwaveWm2": 420 }
  ],
  "bestChargingWindows": [
    { "start": "2026-01-13T11:00", "end": "2026-01-13T14:00", "averageShortwaveWm2": 280 }
  ]
}

get_route_weather

Returns weather aggregated along a route.

By default, the route uses the points you provide. Sampling only happens when you set sampling. With sampling: "auto", the tool resamples the route only if the total route length is at least 10 km, and it enforces a minimum spacing of 5 km (or 5 miles when units is imperial). If you want zero resampling, omit sampling entirely.

Provides:

• Worst-case temperature, wind, rain and precipitation probability per hour
• Optional per-point time series
• Optional ETA-based summaries along the route
• Risk flags based on thresholds

Input example:

{
  "route": [[50.745, 3.6], [51.219, 4.402]],
  "sampling": "auto",
  "metrics": ["temperature", "wind_gusts", "precipitation_probability", "rain"],
  "departureTime": "2026-01-13T12:00",
  "averageSpeedKph": 80,
  "includePointSummary": true,
  "timezone": "auto"
}

Output example (perPointSummary truncated):

{
  "samplesUsed": 16,
  "routeLengthEstimateKm": 76.9,
  "perHourWorstCase": [
    { "time": "2026-01-13T12:00", "temperatureMax": 7.1, "windGustsMax": 32, "precipitationProbabilityMax": 45 }
  ],
  "routeSummary": {
    "mode": "eta",
    "departureTime": "2026-01-13T12:00",
    "averageSpeedKph": 80,
    "maxWindGusts": 32,
    "maxPrecipitationProbability": 45,
    "rainLikely": false,
    "highWindLikely": false,
    "snowLikely": false,
    "hotspots": []
  },
  "perPointSummary": [
    { "pointIndex": 0, "lat": 50.745, "lon": 3.6, "distanceKm": 0, "timeWindow": { "start": "2026-01-13T12:00", "end": "2026-01-13T12:00" }, "temperatureMin": 6.8, "temperatureMax": 6.8 },
    { "pointIndex": 1, "lat": 50.781, "lon": 3.657, "distanceKm": 5.1, "timeWindow": { "start": "2026-01-13T12:04", "end": "2026-01-13T12:04" }, "temperatureMin": 6.7, "temperatureMax": 6.7 }
  ]
}

Note: perPointSummary includes one entry per sample (16 in this example). Only the first 2 are shown above.

get_area_weather

Returns weather aggregated over an area.

The area is sampled as a grid of GPS points.

Provides:

• Hourly and daily aggregates across the grid
• Maximum and average values per field
• Risk flags based on thresholds

Input example:

{
  "boundingBox": { "minLat": 50.6, "maxLat": 50.9, "minLon": 3.4, "maxLon": 3.9 },
  "grid": { "rows": 3, "cols": 3 },
  "fields": ["temperature", "wind_gusts", "precipitation_probability", "rain"],
  "forecastDays": 2,
  "timezone": "auto"
}

Output example:

{
  "gridSize": { "rows": 3, "cols": 3 },
  "pointsUsed": 9,
  "units": { "system": "metric", "temperature": "C", "windSpeed": "km/h", "precipitation": "mm", "distance": "km" },
  "aggregatedHourly": [
    { "time": "2026-01-13T12:00", "avgTemperature": 6.3, "maxWindGust": 30, "maxPrecipitationProbability": 42, "maxRain": 1.2, "maxSnowfall": 0 }
  ],
  "aggregatedDaily": [
    { "date": "2026-01-13", "avgTemperature": 6.1, "maxWindGust": 33, "maxPrecipitationProbability": 58, "rainSum": 2.4, "snowfallSum": 0 }
  ],
  "riskFlags": []
}

get_risk_flags

Returns standardized hazards for a location, route or area.

Provides:

• Hazard type (wind, rain, heat, cold, air quality, visibility)
• Severity
• Time window
• Explanation

Input example:

{
  "lat": 50.745,
  "lon": 3.6,
  "startDate": "2026-01-13",
  "endDate": "2026-01-14",
  "timezone": "auto"
}

Output example:

{
  "units": { "system": "metric" },
  "hazards": [
    {
      "type": "high_wind",
      "severity": "moderate",
      "timeWindow": { "start": "2026-01-13T18:00", "end": "2026-01-13T18:00" },
      "explanation": "Wind gusts reach 71.0 km/h (threshold 70 km/h).",
      "value": 71,
      "threshold": 70
    }
  ]
}

Risk thresholds

Default thresholds are applied to derive hazards:

• Wind gusts (windGustsKph)
• Rainfall rate (rainMmPerHour)
• Precipitation probability (precipitationProbability)
• Heat index (heatIndexC)
• Freezing temperature (freezeTempC)
• Air quality (aqi)
• Visibility (visibilityKm)

Thresholds can be overridden per request.

Pricing (Pay-Per-Event)

Pricing is based on how many locations are analyzed.

• A single GPS location = 1 event
• Routes and areas scale with how many locations they contain
• Risk flags are free when derived from existing results

In other words:
1 event = 1 location of weather intelligence.

Output dataset

Every MCP call is written to the default dataset with:

• Input summary
• Output summary
• Charged events
• Timestamp