Tomorrow.io Extended MCP. Plan logistics around real-time environmental conditions.

Tomorrow.io Extended hands your AI client access to hyper-local, minute-by-minute environmental intelligence. You're not just getting a general forecast; you’re getting actionable data streams for everything outdoors. If your agent needs to plan anything—whether it’s a supply run or running farming operations—it pulls this stuff directly from the source.

Getting Real-Time Conditions: When you need immediate stats, the get_realtime_weather tool feeds you live data points. You get temperature readings, humidity levels, and wind speed for any area right now. This tells your agent what's happening on site at this exact moment.

Forecasting Future Weather Risk: Planning ahead means using get_weather_forecast. This tool gives detailed predictions of future weather conditions, letting you customize the time steps—you can pull hourly updates or full daily outlooks. Your agent uses these details to plan around predicted changes, like when wind speeds are expected to spike or if rain is coming in a few hours.

Analyzing Environmental History: You don't just look at today; you need context. The get_recent_history tool gathers historical weather records. It lets your AI client track environmental patterns and spot trends that happened over the last 24 hours, helping you understand typical operating conditions for a given time of day.

Monitoring Air Safety: Knowing the air quality is critical, so we built in get_air_quality. This tool reports current pollution levels and provides the precise Air Quality Index (AQI) for any city or zone. You'll know if the environment is safe for breathing or if your team needs to take precautions.

Tracking Severe Alerts: Safety first, always. If there’s a severe weather warning out, you need to know it immediately. The list_weather_alerts tool checks and lists all active severe warnings issued for specific geographic areas, so your agent never misses an evacuation or preparation deadline.

This whole setup lets you run complex environmental queries without having to open up five different apps. Your agent uses this single connection to manage everything from current temperature readings via get_realtime_weather to checking if a hurricane warning is active through list_weather_alerts. It's tough; it’s all in one place.

Your AI client can cross-reference multiple data streams. For instance, you could ask your agent: 'What was the average wind speed over the last 24 hours (get_recent_history) compared to the predicted conditions for tomorrow afternoon (get_weather_forecast), and is the air quality currently acceptable (get_air_quality)'—all in one prompt. The server handles it.

The system's strength lies in its granularity. You’re not getting vague regional averages; you’re pulling hyper-local, minute-by-minute intelligence that matters when time counts. Whether your goal is assessing agricultural yield based on historical rainfall patterns or just sending people out safely today, the data supports it.

It's built to feed directly into your agent's logic: detecting a drop in AQI might trigger a message via your client; seeing high wind speeds predicted hourly might automatically adjust a drone flight path. The combination of detailed forecasting (get_weather_forecast), instant readings (get_realtime_weather), and historical context (get_recent_history) gives you the operational picture you need to make tough, time-sensitive calls.