WindAI MCP Server — AI-powered wind resource assessment tools for Claude, ChatGPT, and other AI assistants. Get wind speed estimates, capacity factors, and energy production predictions for any location on Earth.
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