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
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 mcp add windai -- npx -y windai-mcpAdd a similar configuration using npx -y windai-mcp as the command.
npm install -g windai-mcp
windai-mcpRelated MCP server: WeatherAI MCP
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.
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.
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
git clone <repo-url>
cd windai-mcp
npm install
npm run devBuild for production:
npm run build
npm startMIT
Compare wind potential at multiple locations side by side. Up to 5 locations. Returns a ranked comparison table with mean wind speed, IEC wind class, wind quality, and wind power density for each site. Free, no API key required.
Inputs: locations (required).
Get information about WindAI's machine learning model, including architecture, training data, accuracy metrics, and validation results. Useful for understanding the methodology behind WindAI predictions.
Inputs: no named parameters.
Get WindAI pricing information for wind resource assessments. Shows available credit packages, what's included in each assessment, and how to get started.
Inputs: no named parameters.
Get an approximate wind resource estimate for any location on Earth. Returns mean wind speed, IEC wind class, wind quality assessment, monthly wind speeds, and wind power density. Uses 3 years of historical reanalysis data (2021-2023). Free, no API key required.
Inputs: latitude (required), longitude (required), hub_height.
Run a full WindAI AI-powered wind resource assessment using our deep learning model trained on 10M+ hourly observations from 289 wind farms. Returns hourly capacity factors (8,760+ hours), AEP, P50/P90, monthly and diurnal profiles, and comprehensive wind farm performance metrics. Requires a WindAI API key (get one at https://windai.tech/account).
Inputs: latitude (required), longitude (required), api_key (required), hub_height, +5 more.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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Scored across 5 tools
Each tool has a clearly distinct purpose: get_wind_estimate provides free approximate data, get_wind_farm_assessment provides detailed paid analysis, compare_wind_sites handles multi-location comparison, and the remaining two provide service metadata (pricing, model info). No functional overlap exists.
All tools use snake_case with verb_noun structure (get_, compare_). The 'windai' prefix appears only on meta-tools (model_info, pricing) distinguishing service information from wind data tools, which is logical but slightly unconventional.
Five tools is an ideal count for this specialized domain: three core wind assessment functions (quick estimate, detailed assessment, comparison) plus two supporting tools (pricing, methodology). No bloat, no missing essentials.
Covers the core wind resource assessment workflow well (screening → detailed analysis → comparison). Minor gaps include no account/credit balance check tool (relevant since assessments require paid credits) and no ability to retrieve past assessment history.
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