DNV Renewables MCP for AI. Quantify Global Wind and Solar Resource Potential
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DNV Renewables accesses world-class climate data for wind and solar resource assessment. It delivers mesoscale climate datasets, energy yield estimates, and global resource metrics needed for project feasibility studies.
Use it to quantify renewable potential for both onshore and offshore sites worldwide.
What your AI can do
Check data availability
Determines if wind or solar datasets are available for a given location before you order anything.
Download order data
Downloads the time series file after an ordered data request has been successfully completed.
Get energy yield estimate
Calculates the estimated annual energy production for a wind turbine at a specific site.
Get detailed wind speed, direction, and temperature metrics for any global point.
Access essential solar data like GHI and DNI to plan photovoltaic (PV) systems.
Calculate the annual energy production estimate for wind turbines given site parameters.
Pull long-term mesoscale climate model data necessary for deep resource analysis.
Check the status of large data orders, place new requests, and download completed files.
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DNV Renewables: 11 Specialized Tools
These tools let your agent perform every step of resource assessment, from checking data availability to running energy yield calculations.
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 DNV Renewables on VinkiusCheck Data Availability
Determines if wind or solar datasets are available for a given location before you order anything.
Download Order Data
Downloads the time series file after an ordered data request has been successfully...
Get Energy Yield Estimate
Calculates the estimated annual energy production for a wind turbine at a specific...
Get Order Status
Checks the current status of a data order—whether it's pending, processing, or ready...
List Available Datasets
Lists all 40+ types of climate and renewable energy datasets available for querying.
List All Orders
Shows an overview list of every data order you've placed within your account history.
Locate Data Nodes
Pinpoints the exact geographic coverage areas and data nodes for a specific dataset type.
Get Mesoscale Climate Data
Retrieves long-term climate model data, useful for broad environmental analysis over...
Place Data Order
Submits an order to extract large volumes of climate or resource data, generating a...
Get Solar Resource Data
Fetches essential solar resource metrics needed for designing photovoltaic systems.
Get Wind Resource Data
Retrieves necessary wind data to assess the suitability and potential of a specific...
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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by DNV Renewables. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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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 11 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Sourcing Global Climate Data Used To Be an Academic Nightmare
Today, building a feasibility report means hunting down datasets from dozens of academic sources. You copy-paste coordinates into one site for wind data, then manually search another source for solar irradiance; you spend days compiling PDFs and cross-referencing date ranges just to get the inputs ready.
With this MCP, your agent handles that entire process in a single interaction. It checks availability, pulls multiple resource types like mesoscale climate data, and organizes everything into a clean output file, letting you focus on analysis instead of administration.
Get Energy Yield Estimates
Before this MCP, calculating the projected energy yield required taking specialized software inputs—turbine size, hub height, and local wind speed averages—and running them through a complex model. This was slow, expensive, and often limited by manual data entry.
Now, you simply provide the site details to your agent; it runs `get_energy_yield_estimate` using the best available resource inputs and gives you an immediate, quantified annual energy production range. The complexity disappears.
What your AI can actually do with this
This MCP lets your agent pull professional-grade resource intelligence directly into your workflow. Instead of sifting through disparate academic reports or expensive physical data subscriptions, you access verified wind speed profiles, solar irradiance levels (GHI/DNI), and long-term climate model data for any location on Earth.
It handles the entire spectrum of site assessment: from checking if a specific dataset exists at your coordinates to running complex calculations that estimate annual energy production. You can request time series files covering custom historical periods, which is critical when modeling against past climatic variability. When you use this MCP through Vinkius, your AI agent becomes an immediate resource analyst, allowing you to compare wind and solar potential across dozens of sites without ever leaving your chat window or IDE.
019d7587-0d74-73bf-9e9a-32bf15a8c843 Here's how it actually works
The bottom line is you guide your agent through a structured process: check availability, order data, and then analyze the results.
First, run check_data_availability to confirm that the required wind or solar datasets exist for your specific coordinates.
Next, use place_data_order to request multi-year time series data. You track its progress using get_order_status until it shows success.
Finally, download the completed file via download_order_data, or use the raw inputs with tools like get_energy_yield_estimate to run calculations.
Who is this actually for?
Wind and solar developers who need to prove site viability; energy consultants needing standardized resource reports; or academic researchers studying climate impact on power generation. This MCP eliminates weeks of manual data gathering.
Needs to generate professional, defensible resource assessments for clients evaluating new project sites.
Must assess wind resource potential and calculate expected energy yields before committing capital to a site.
Requires detailed solar irradiance data (GHI, DNI) to accurately size PV arrays and model output.
What Changes When You Connect
Stop guessing resource potential. You can get precise wind metrics using get_wind_resource_data or solar irradiance data via get_solar_resource_data, allowing for accurate initial site screening.
The calculation step is simple: Instead of writing complex formulas, your agent runs get_energy_yield_estimate to immediately calculate the projected annual energy production (AEP).
Managing large datasets doesn't require manual follow-up. Use list_all_orders and then get_order_status to track multi-year data requests from start to finish.
The scope is global, not local. You can access mesoscale climate data using get_mesoscale_climate_data, supporting research that needs decades of long-term atmospheric context.
You aren't limited by what's in your database. By running list_available_datasets, you see over 40 types of resources, giving the deepest possible technical scope.
See it in action
Feasibility Check for a Remote Site
A developer needs to check if a remote mountain site can support a wind farm. They first run check_data_availability using the coordinates, confirming necessary datasets exist. Then they use get_wind_resource_data and proceed straight to running get_energy_yield_estimate for an initial report.
Academic Climate Trend Analysis
A researcher studying climate change needs historical data on solar fluctuations. They use list_available_datasets to find the right proxy, then run get_mesoscale_climate_data for a 50-year time slice to model long-term resource shifts.
Developing an Offshore Project
A utility company plans an offshore build. They use get_wind_resource_data and then place a large data order via place_data_order, checking the status with get_order_status until the full dataset is ready for download.
Comparing Wind vs. Solar Viability
A consultant needs to advise a client on whether wind or solar is better at two different sites. They run get_solar_resource_data for Site A and get_wind_resource_data for Site B, comparing the output metrics side-by-side.
The honest tradeoffs
Assuming data is ready
Running download_order_data immediately after submitting a request. The API will fail because the files aren't processed yet.
Always check status first. After using place_data_order, you must use get_order_status. Only when it reports 'success' can you run download_order_data.
Ignoring scope limitations
Trying to find a specific dataset type that isn't listed in the documentation. You waste time guessing coordinates.
Start by running list_available_datasets. This shows you all 40+ available datasets, ensuring your query uses recognized resource types.
Skipping initial validation
Running complex calculations like get_energy_yield_estimate without knowing if the required wind data exists at that location. The calculation will fail.
First, always validate scope using check_data_availability. This confirms both resource type and time period viability before running any estimates.
When It Fits, When It Doesn't
Use this MCP if your core need is acquiring large-scale, verifiable physical data: wind speed measurements, solar irradiance levels, or long-term climate models. The moment you need to combine this raw resource data with financial modeling (e.g., calculating ROI), you've successfully gathered the required inputs.
Don't use it if your problem is purely organizational; for example, if you just need to compare two client reports written in PDFs—use a document analysis tool instead. Also, don't rely on this MCP to generate final engineering drawings or compliance documents; its output is raw data and estimates only.
If you are simply trying to find out what kind of data exists for a region, use list_available_datasets. If you need to calculate the potential based on existing data, run get_energy_yield_estimate. This tool chain is strictly for resource assessment; it's not a full project management suite.
Questions you might have
What types of wind and solar data are available? +
DNV Renewables provides over 40 climate datasets including: Mesoscale wind data (global and regional), Solar irradiance (GHI, DNI, DHI), Reanalysis datasets (ERA5, MERRA-2), Global wind energy atlas, Temperature, pressure, humidity, and more. Data covers onshore and offshore locations worldwide with time series from 1980s to present.
How do I get an API token for DNV Renewables? +
Visit the DNV Renewables platform and contact your DNV account manager or visit the EMD/DNV Renewables website to request API access. Once your account is provisioned, you'll receive an API access token from your account dashboard. This token authenticates your requests and is linked to your subscription plan.
How does the data ordering process work? +
The data ordering process is: 1) Check availability for your location, 2) Place an order with dataset, coordinates, and time period, 3) Wait ~30 seconds for processing, 4) Check order status until 'success', 5) Download the generated time series file. Note: Download links expire after 12 hours, so download promptly. Rate limit is 10 orders per 10 minutes.
What is mesoscale climate data and why is it useful? +
Mesoscale climate data comes from numerical weather prediction models that simulate atmospheric conditions at regional scales (typically 1-50 km resolution). Unlike single-point measurements, mesoscale data provides spatially consistent, long-term time series essential for renewable energy resource assessment. It's used for wind farm siting, solar project planning, and long-term energy yield analysis because it captures multi-decadal climate patterns that short-term measurements miss.
How can I confirm if a dataset is actually available at my specific site using `locate_data_nodes`? +
It shows the precise geographic coverage and data nodes for any dataset. This function tells you exactly where the model was trained or measured, preventing you from ordering data for an area outside its spatial resolution.
What happens if I run `get_order_status` and my file is ready to download? +
The status confirms success and provides a temporary URL for the completed file. Remember, these files auto-delete after twelve hours; you need to use download_order_data quickly.
Before I place an order using `place_data_order`, should I run `check_data_availability` first? +
Yes, always check availability first. Running this function verifies the necessary variables and time periods for your specific location before you waste credits on a full data order.
Does `get_wind_resource_data` cover both onshore and offshore wind sites globally? +
It covers global resources for both onshore and offshore locations. However, remember that accurate results require very specific latitude and longitude coordinates to pinpoint the exact site conditions.
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