About Weather Edge Mcp
Weather Edge is an MCP server that turns public weather forecasts and Kalshi market data into calibrated signals for AI agents analyzing weather markets. It estimates probabilities, market edge, and expected value for supported cities.
Key features
- 01Calibrated NWS temperature forecasts
- 02Kalshi market price retrieval
- 03Bucket probability and edge estimates
- 04City-specific forecasts and METAR data
- 05Optional FastAPI endpoints
Use cases
- 01Assessing Kalshi weather market opportunities
- 02Reviewing forecast context for a city
- 03Checking the latest settlement-station observation
MCP support
Exposes weather signals, forecasts, station observations, and supported-city information as MCP tools. It supports stdio, SSE, and streamable HTTP transports.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEWeather 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
Comments
New to MCP?
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.