Xweather Renewable MCP for AI. Forecast Energy Yields and Assess Site Viability.
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Xweather Renewable gives you professional weather intelligence for renewable energy sites. Get current conditions, 15-day forecasts, solar irradiance data (GHI, DNI, DHI), and wind measurements needed to assess power plants.
This MCP supports site selection, production forecasting, and grid optimization.
What your AI can do
Get weather alerts
Checks for active weather advisories and warnings that could threaten renewable energy assets in an area.
Get closest weather station
Finds the nearest operational weather station and returns its details, including current conditions.
Get current conditions
Retrieves real-time weather metrics using a city name, coordinates, or known station ID.
Locates the closest operational weather station and returns its current conditions for immediate use.
Retrieves up-to-the-minute temperature, wind speed, humidity, pressure, and solar radiation for any specified location.
Gets a detailed 15-day weather forecast tailored for predicting renewable energy production over time.
Queries archived data to measure past solar irradiance or general weather patterns, essential for model accuracy checks.
Provides detailed measurements of wind speed and direction required for evaluating turbine placement and output.
Retrieves active weather alerts and advisories, helping protect assets during dangerous conditions.
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Xweather Renewable: 12 Tools for Energy Assessment
These tools allow you to pull everything from current weather metrics and detailed wind speed data to historical solar irradiance measurements needed for professional energy planning.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Xweather Renewable on VinkiusGet Weather Alerts
Checks for active weather advisories and warnings that could threaten renewable energy assets in an area.
Get Closest Weather Station
Finds the nearest operational weather station and returns its details, including...
Get Current Conditions
Retrieves real-time weather metrics using a city name, coordinates, or known station...
Get Renewable Energy Farm Data
Provides hourly forecasts and recent estimates of power output for wind and solar...
Get Extended Forecast
Generates a 15-day forecast with day/night periods for long-term energy production...
Get Weather Forecast
Gets a general 15-day weather outlook, useful for broad production planning across large areas.
Get Historical Observations
Pulls archived weather data, useful for validating your renewable energy models against past patterns.
Get Weather Observations
Pulls recent actual data measured by specific weather stations for the location you...
Search Locations
Searches by name or coordinates to find place details, including the necessary...
Get Solar Irradiance Data
Retrieves historical measurements of solar irradiance (GHI, DNI, DHI) needed to...
Get Weather Summary
Provides a quick, general overview of expected weather conditions for any given...
Get Wind Data
Gathers specific wind speed and direction data crucial for assessing wind farm performance.
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Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
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Start with Xweather Renewable, then connect any of our 5,000+ other servers whenever your AI needs more. One click, no limits.
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- Works with Claude, ChatGPT, Cursor, and more
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 12 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
The constant battle of checking dashboards across different services.
Right now, to build a comprehensive forecast for a new wind farm site, you're clicking between the GIS platform, the weather service API, and the energy modeling suite. You manually copy coordinates from one tab into another, wait for three different data streams—wind speed, solar potential, and historical pressure—to load, and then you spend an hour trying to piece together a reliable single view.
With this MCP, your agent handles the whole sequence. You ask it to assess Site X, and it pulls in all the necessary inputs automatically: current readings via `get_current_conditions`, long-term projections with `get_extended_forecast`, and historical data using `get_historical_observations`. The output is a single, cohesive report ready for your team.
Get Solar Irradiance Data: Proof of Energy Potential
Previously, determining if a solar site was truly viable meant commissioning expensive physical studies or relying on generalized resource maps. You'd get estimates, not hard data showing the actual energy potential over time.
Now you use `get_solar_irradiance_data`. This tool pulls validated measurements like GHI and DNI for your specific coordinates. It moves solar assessment from 'this looks good enough' to 'the numbers prove it.' That’s a massive difference.
What your AI can actually do with this
This connector brings deep, professional weather intelligence directly into your AI agent. You can run complex analyses for renewable energy sites—from initial assessment to operational planning. Need to know if a PV farm is viable? Pull historical solar irradiance measurements (GHI, DNI, DHI) to validate its potential yield. Planning long-term maintenance? Get extended 15-day forecasts that cover day and night cycles.
The system also pulls wind speed, direction, and detailed energy generation estimates for US/Canada sites. Because data integrity is everything when running production models, every tool call passes through the Vinkius zero-trust proxy; your keys are used in transit but never stored on disk. This means you can trust that sensitive site credentials remain secure while building complex automations.
019d7625-d864-733f-b875-2995ded81d50 Here's how it actually works
The bottom line is, you get professional-grade energy data for site assessment and operational decision-making without leaving your AI client.
First, use search_locations to find the exact station ID or coordinates for your site.
Next, select the necessary data stream—whether it's running a historical query via get_solar_irradiance_data or getting immediate readings with get_current_conditions.
Finally, the agent processes the output to give you actionable insights, like determining if current wind patterns support optimal turbine operation.
Who is this actually for?
Energy developers and grid operators who hate running multiple dashboards to piece together a single production forecast. This MCP lets you build predictive models from the chat window.
Uses get_solar_irradiance_data to assess if a new parcel of land has enough historical solar resource quality for PV development.
Runs get_wind_data and get_extended_forecast to model potential output changes over the next two weeks, adjusting turbine placement as needed.
Checks get_weather_alerts alongside current conditions to predict how incoming renewable generation might affect local grid stability.
What Changes When You Connect
Assess initial site potential using get_solar_irradiance_data to measure historical solar resource quality, which is better than just checking a map image.
Plan for months ahead by running get_extended_forecast, getting day/night breakdowns that let you model production dips accurately. You can't plan without the full cycle view.
Optimize energy trading models using get_renewable_energy_farm_data to predict hourly output, giving you a massive edge in market timing.
Protect infrastructure by monitoring active threats; calling get_weather_alerts ensures your agent knows exactly when severe weather is hitting the grid.
Go beyond simple summaries. Use get_wind_data to get specific directional and speed metrics that dictate how much power a turbine can actually generate.
See it in action
Comparing two sites for solar development
You need to know if Site A or Site B is better. First, use search_locations to get the coordinates for both. Then, run get_solar_irradiance_data on both sets of coordinates to compare their historical GHI and DNI metrics side-by-side.
Responding to an unexpected storm warning
A sudden weather event is reported. Immediately use get_weather_alerts for the area, then cross-reference that with current readings from get_current_conditions to gauge immediate risk and operational shutdown needs.
Modeling annual wind capacity
To calculate total potential output, you must pull long-term data. Use get_wind_data for specific metrics, then run get_historical_observations to establish a reliable baseline against multiple years of archived readings.
Running a quick check on day-to-day operations
Before the morning crew arrives, use get_weather_summary for general awareness. If that looks okay, then follow up with get_renewable_energy_farm_data to get specific operational forecasts for the next 10 days.
The honest tradeoffs
Assuming current data is enough
Running only get_current_conditions and thinking you have a full picture of risk or future yield.
Don't stop there. Always follow up with get_weather_alerts to check for incoming threats, and then use get_extended_forecast to see how the next two weeks are shaping up.
Using general search tools
Searching Google for 'solar weather in Miami' because you think an AI agent can figure it out.
You need structured data. Start by calling search_locations to get the exact coordinates, then use those coordinates with get_solar_irradiance_data for verifiable metrics.
Ignoring historical context
Using only a 7-day forecast when you need to prove long-term viability for investors.
For proof, run get_historical_observations. This gives validated data that supports your claims far better than any short-term prediction.
When It Fits, When It Doesn't
Use this MCP if the core problem is predicting energy output or assessing site resource quality. You need metrics like GHI/DNI, wind speed/direction, or multi-day forecasts; those are your primary indicators. Don't use it if you just need basic travel information (e.g., 'What's the high temp today?'). For simple checks, get_weather_summary is fine. But for anything involving capital expenditure, production modeling, or risk assessment, stick to this MCP and its specific tools. Never rely on a single data point; cross-reference get_current_conditions with both get_extended_forecast and get_weather_alerts for the full picture.
Questions you might have
How do I check the current weather and wind speed using get_current_conditions? +
Just provide the city name, coordinates, or station ID to get_current_conditions. The agent returns real-time metrics like temperature, humidity, pressure, and detailed wind speed/direction.
What is the difference between get_weather_forecast and get_extended_forecast? +
get_weather_forecast gives a general 15-day outlook. get_extended_forecast is better because it provides day/night periods, which is critical for accurate energy production modeling.
Can I check historical data using get_historical_observations? +
Yes. Use get_historical_observations to query archived weather records. This allows you to validate your current models against validated, past performance metrics.
Which tool should I use for wind resource planning? get_wind_data or get_extended_forecast? +
get_wind_data is the specialized choice. It gives specific speed and direction measurements essential for turbine placement, whereas get_extended_forecast provides general weather context.
How do I find coordinates before using any weather tool? +
Start with search_locations. This function searches by name or query and returns the necessary coordinates and station metadata required for all subsequent data calls.
When I use get_solar_irradiance_data, how do I interpret the difference between GHI, DNI, and DHI? +
GHI (Global Horizontal Irradiance) is the total energy hitting a horizontal surface. DNI (Direct Normal Irradiance) measures direct beam sunlight, which is crucial for concentrating solar power. DHI (Diffuse Horizontal Irradiance) accounts for scattered light from the atmosphere.
If I check for warnings using get_weather_alerts, what specific kind of risk data do I receive? +
This tool provides critical, actionable advisories regarding severe weather events. It doesn't just report the weather; it tells you exactly which hazards—like high winds or flooding—are active at your specified location.
Is the data from get_renewable_energy_farm_data reliable enough for actual energy trading decisions? +
Yes, this MCP includes hourly forecasts and recent 5-minute interval production estimates. This high level of detail makes it essential for operational optimization and real-time energy market planning.
What weather and renewable energy data is available through the Xweather API? +
The Xweather API provides: current conditions (temperature, humidity, wind, pressure, solar radiation), forecasts up to 15 days, historical weather observations, solar irradiance data (GHI, DNI, DHI) for PV assessment, wind speed/direction measurements, renewable energy farm power output data for US/Canada sites, weather alerts and advisories, and location search capabilities.
How do I get Xweather API credentials (Client ID and Client Secret)? +
Visit https://www.xweather.com/ and sign up for an Xweather Flex subscription. Once your account is provisioned, navigate to your developer dashboard to create an application and obtain your Client ID and Client Secret. These credentials authenticate your API requests and are tied to your subscription plan. The Renewables Add-on is required for energy farm data access.
What location formats are supported for weather queries? +
The API accepts multiple location formats: city names (e.g., 'Chicago,IL'), latitude/longitude coordinates (e.g., '41.88,-87.63'), weather station IDs, postal codes, and ICAO airport codes. You can also use the searchPlaces tool to find valid location identifiers or the getClosestStation tool to find the nearest weather station to any coordinates.
How far ahead can the Xweather forecast predict? +
Xweather provides detailed weather forecasts up to 15 days ahead. Short-term forecasts (1-7 days) are the most accurate, with gradually decreasing accuracy for longer horizons. The forecast includes temperature, weather conditions, wind, precipitation, and solar radiation data — all essential for renewable energy production planning.
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