Tomorrow.io MCP. Predicting Hazards, Not Just Temperature.

Look, if your logic flow depends on knowing what the weather’s gonna do—and you know it does—this connector feeds all that intelligence right into your agent. You're getting institutional-grade data from Tomorrow.io, meaning you don't just get surface-level forecasts; you get hyper-specific environmental matrices for everything from pollen counts to road risk.

Getting Current Atmospheric Readings:
You can pull real-time conditions using get_realtime_weather. Just give it a location—a zip code, city, or lat/lon—and you'll get the current temperature, wind speed, pressure, and other atmospheric indicators right now. It’s what you use when your agent needs to know if it's raining this second.

Predicting Future Weather Patterns:
When predicting out, you've got options for every timescale. If you need a quick look at the big picture, get_daily_forecast gives you daily extremes and totals spanning up to 15 days. You can zoom in way deeper if you use get_hourly_forecast, which returns predictions hour-by-hour for up to 120 hours out.

But sometimes, minutes matter—and they do. Use get_minutely_precipitation when you need minute-by-minute rain forecasts (nowcasts) for super precise planning.

If the standard forecast tools don't cover the exact window your agent needs, use get_custom_timelines. This lets you query weather data over custom time ranges—you specify the arbitrary intervals, and it pulls the data. You’ll also want to check out specialized environmental forecasts: get_air_quality_index fetches current and forecasted air quality index (AQI) for any location.

For allergy season planning, get_pollen_forecast gives you daily predictions specifically for pollen counts. And if there's a wildfire brewing, get_wildfire_risk pulls the current risk index alongside associated weather conditions.

Analyzing Road Travel Risk:
You can’t just look at the temperature and assume the roads are fine. You need to run specialized checks on road hazards using get_road_weather_risk. This tool generates specific assessments for driving safety, helping you plan routes around complex weather patterns like flash flooding or icy conditions.

Querying Environmental History:
Need to audit something? Want to train a model based on what actually happened last fall? Use get_historical_weather. You can retrieve actual recorded observations by specifying past dates and locations, letting your agent analyze outcomes from any time period in the past. This isn't theoretical data; it’s real-world records.

Ultimately, this server gives your AI client everything it needs—from immediate atmospheric readings to 15-day projections, specialized hazard reports, and historical benchmarks. You'll feed all that depth directly into your agent's decision-making process.