About peak-window
peak-window is an MCP App for analyzing weather at alpine peaks and trailheads worldwide, scoring hourly forecasts to rank climbing windows. It selects a regional forecast model and can render interactive charts in hosts that support MCP Apps.
Key features
- 01Fetch hourly weather forecasts
- 02Select forecast models by region
- 03Fall back when an API fails
- 04Adjust temperature for summit elevation
- 05Score and rank weather windows
- 06Render embedded interactive charts
Use cases
- 01Compare climbing windows for a peak this week
- 02Check precipitation, wind, and snow-line forecasts
- 03Inspect weather time series in charts
- 04Query peak weather across the globe
MCP support
Its MCP tool accepts coordinates and related inputs, returning scored hours, top weather windows, and a time series. Hosts that support the MCP Apps protocol can render its interactive UI as a resource; other clients still receive text output.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEPeakWindow MCP App
An MCP App that analyzes upcoming weather at alpine peaks and trailheads worldwide, ranking the best windows for climbing and ascent. Uses the best available forecast model for each region: GeoSphere Austria AROME (2.5 km) for Central Europe, MeteoSwiss ICON-CH2 (2 km) for the Alpine region, Météo-France AROME (2.5 km) for France, and Open-Meteo globally (~11 km).
What it does
- Fetches hourly forecasts (temperature, precipitation, wind, gusts, cloud cover, snow line) from the best available provider for the requested coordinates
- Automatically selects the highest-resolution model: GeoSphere (2.5 km, Central Europe), MeteoSwiss (2 km, Alpine region), Météo-France (2.5 km, France), or Open-Meteo (~11 km, global)
- Falls through to the next provider on API failure for resilience
- Lapse-corrects temperatures to summit elevation using model terrain derived from surface pressure (-6.5 °C/km)
- Scores each hour against alpine climbing thresholds and identifies contiguous good-weather windows
- Serves an interactive UI (React, uPlot charts) as an MCP App resource with horizon tape, mountain profile, and Ventusky-style chart panels
Requirements
This is an MCP App — it requires an MCP host that supports the Apps protocol to render its interactive UI. Compatible hosts include:
- Claude Desktop (macOS / Windows)
- ChatGPT (via OpenAI Apps SDK)
- Any chat client implementing the MCP Apps spec
The server runs as a standard MCP server over stdio or SSE. The rich UI (charts, horizon tape, mountain profile) renders inline in any compliant host. Without one you still get the scored text output.
Live UI demo — fetches real forecasts in the browser; pick any peak to see its scored windows and real DEM silhouette.
Setup
Claude Desktop
Add to your claude_desktop_config.json:
{
"mcpServers": {
"peak-window": {
"command": "npx",
"args": ["-y", "peak-window", "--stdio"]
}
}
}
Then ask Claude something like: "What's the best weather window to climb Großglockner this week?"
HTTP server
npx peak-window # starts on http://localhost:3001/mcp
Development
git clone https://github.com/ByteOverDev/peak-window-mcp-app.git
cd peak-window-mcp-app
npm install
npm run dev # hot reload (UI + server)
npm run serve:stdio # stdio transport
MCP tool
peak-window — provide lat, lon, and optionally peakName and summitElevationM. Returns scored hours, top weather windows, and a full time-series payload rendered by the embedded UI.
Data sources
| Provider | Resolution | Coverage | Via |
|---|---|---|---|
| GeoSphere Austria | 2.5 km | Central Europe (5.5–22.1°E, 43–51.8°N) | Direct API |
| MeteoSwiss ICON-CH2 | 2 km | Alpine region (0.5–16.5°E, 43–49.9°N) | Open-Meteo |
| Météo-France AROME | 2.5 km | France & surrounds (-9–14°E, 38–55°N) | Open-Meteo |
| Open-Meteo | ~11 km | Global | Direct API |
Comments
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From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.