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): Latitudelongitude(required): Longitudeapi_key(required): WindAI API key (starts withwai_)hub_height(optional): Hub height in metersrated_power(optional): Turbine rated power in kWrotor_diameter(optional): Rotor diameter in metersturbines_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
Links
License
MIT