WindAI MCP Server

AI-powered wind resource assessment tools for Claude, ChatGPT, Cursor, and other AI assistants via the Model Context Protocol (MCP).

Get wind speed estimates, compare sites, and run full ML-powered wind farm assessments from any MCP-compatible AI assistant.

Website: windai.tech

Quick Start

Claude Desktop

Add to your Claude Desktop configuration (~/Library/Application Support/Claude/claude_desktop_config.json on macOS or %APPDATA%\Claude\claude_desktop_config.json on Windows):

{
  "mcpServers": {
    "windai": {
      "command": "npx",
      "args": ["-y", "windai-mcp"]
    }
  }
}

Restart Claude Desktop, then ask:

"What's the wind potential at latitude 40.5, longitude -105.2?"

Claude Code

claude mcp add windai -- npx -y windai-mcp

Cursor / Other MCP Clients

Add a similar configuration using npx -y windai-mcp as the command.

Global Install

npm install -g windai-mcp
windai-mcp

Tools

`get_wind_estimate` (Free)

Get an approximate wind resource estimate for any location on Earth. No API key required.

Input:

latitude (required): Latitude (-90 to 90)
longitude (required): Longitude (-180 to 180)
hub_height (optional): Hub height in meters (default: 100)

Returns: Mean wind speed, IEC wind class, wind quality assessment, monthly breakdown, wind power density.

Example prompt: "Estimate the wind resource at 52.5N, 1.8E at 120m hub height"

`get_wind_farm_assessment` (Requires API Key)

Run a full AI-powered wind resource assessment using WindAI's deep learning model (391-feature neural network trained on 10M+ hourly observations from 289 wind farms).

Input:

latitude (required): Latitude
longitude (required): Longitude
api_key (required): WindAI API key (starts with wai_)
hub_height (optional): Hub height in meters
rated_power (optional): Turbine rated power in kW
rotor_diameter (optional): Rotor diameter in meters
turbines_count (optional): Number of turbines
Plus: swept_area, total_power

Returns: 8,760+ hourly capacity factors, AEP, P50/P90, monthly and diurnal profiles.

Get an API key: windai.tech/account

`compare_wind_sites` (Free)

Compare wind potential at multiple locations side by side. Up to 5 locations.

Input:

locations (required): Array of { latitude, longitude, name? } objects (2-5 sites)

Returns: Ranked comparison table sorted by wind quality.

Example prompt: "Compare wind potential at these sites: Denver CO (39.7, -105.0), Amarillo TX (35.2, -101.8), and Cheyenne WY (41.1, -104.8)"

`get_windai_pricing` (Free)

Get current pricing information for WindAI assessments.

Returns: Credit packages, per-site pricing, what's included, and signup links.

`get_windai_model_info` (Free)

Get information about WindAI's ML model, training data, and accuracy metrics.

Returns: Architecture details, training data stats, accuracy metrics, validation methodology.

Pricing

Package	Credits	Total	Per Site	Savings
Single	1	$49.99	$49.99	--
Starter	10	$449.90	$44.99	10%
Pro	25	$999.75	$39.99	20%
Enterprise	100	$3,499.00	$34.99	30%

Buy credits at windai.tech/credits.

Data Sources

Free tools: Open-Meteo ERA5 Historical Reanalysis (2021-2023), no API key needed
Paid assessments: WindAI's proprietary deep learning model using ERA5, MERRA2, Copernicus DEM, and turbine specs

Development

git clone <repo-url>
cd windai-mcp
npm install
npm run dev

Build for production:

npm run build
npm start

License

MIT

WindAI MCP Server