Weather Edge MCP Server

Weather Edge MCP Server

Weather Edge is an MCP server for calibrated Kalshi weather-market signals. It turns public forecast and market data into a compact tool surface for AI agents.

What it does

• calibrates NWS daily high-temperature forecasts by city
• reads current Kalshi weather market prices
• estimates per-bucket probability, edge, and net expected value
• exposes the results through MCP tools and an optional FastAPI surface

Install

pip install weather-edge-mcp

MCP usage

Claude Desktop

{
  "mcpServers": {
    "weather-edge": {
      "command": "python",
      "args": ["-m", "weather_edge_mcp"]
    }
  }
}

Other MCP clients

Use either of these commands:

weather-edge-mcp
python -m weather_edge_mcp

Transport options

weather-edge-mcp --transport stdio
weather-edge-mcp --transport sse --port 8050
weather-edge-mcp --transport streamable-http --port 8050

Tools

Tool	Description
get_weather_signals(city)	Calibrated signals for one city's Kalshi weather markets
get_all_signals()	Full scan across all supported cities
get_forecast(city)	Bias-adjusted forecast context for one supported city
get_station_observation(city)	Latest METAR observation from the settlement station
list_cities()	Supported cities and calibration parameters

Supported cities: nyc, chicago, denver, miami, la

Optional web API

Weather Edge also ships an optional FastAPI app:

python -m uvicorn weather_edge_mcp.web_app:app --host 0.0.0.0 --port 8080

Routes:

• /api/health
• /api/signals?city=nyc
• /api/all-signals
• /dashboard
• /subscribe

If the optional x402 stack is installed and configured, the paid routes can be gated there. MCP stdio mode stays clean and side-effect free.

Docker

The repo includes a Dockerfile for Glama/container builds.

docker build -t weather-edge-mcp .
docker run --rm weather-edge-mcp --help

Architecture

src/weather_edge_mcp/
  core.py        # forecasting, market fetches, calibration, formatting
  mcp_server.py  # MCP tools
  web_app.py     # optional FastAPI surface
  cli.py         # command-line entrypoint

Data sources

• National Weather Service forecast API
• Aviation Weather METAR API
• Kalshi public market API

Development

python -m unittest discover -s tests -v
python -m build

License

MIT