Xweather Renewable MCP. Model energy output with real-world weather data.

Xweather Renewable MCP Server - Weather & Energy Forecasts

Listen up. When you're running energy models, you can't mess around with bad data. This server connects your AI client directly to Vaisala’s professional weather intelligence, giving your agent access to real-time and forecasted metrics for any renewable site. It's built for people who need hard numbers—not marketing fluff.

To start, if you don't know exactly where you are, use search_locations to find place details and coordinates. Once you have those, you can run the get_closest_weather_station tool; it pulls up the station nearest your spot and gives you its current conditions. You can also get a quick read on general weather using get_weather_summary, or pull actual, recent readings straight from an operational site with get_weather_observations.

For real-time status—like temperature, wind speed, humidity, or solar radiation for a specific city or set of coordinates—you just run get_current_conditions.

When you need to plan out weeks ahead, the server gives you two options. First, use get_weather_forecast or get_extended_forecast; these deliver multi-day forecasts up to 15 days, detailing both day and night periods so you can nail down long-term operations planning. For a general overview of what's coming, you can run the more broad get_weather_forecast.

For assessing the physical resources—that’s where it gets critical. If you're checking out a new solar site, use get_solar_irradiance_data to get historical metrics on GHI, DNI, and DHI. These numbers are non-negotiable for validating a PV site's actual potential. When you're focused on wind power, the get_wind_data tool gives you detailed measurements of speed, direction, and even gust data—it’s what you need to model turbine placement.

For overall energy planning at large scale, get_renewable_energy_farm_data handles the estimated or forecasted power output for both wind and solar farms.

But it's not just about predicting; it's about validating and managing risk. You can run get_historical_observations to query archived data points, letting your agent validate production models against past performance trends. Before you commit to anything, check the books with get_weather_alerts; this tool runs severe weather warnings that could trash your assets in a specific area.

It's all about making sure your forecast is airtight. You can use get_weather_observations for immediate data confirmation and combine it with the general location finding of search_locations to build out any complex energy model you need.