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Weather Edge MCP Server

@RJW34

Repository

Basic information

Category

Media & Design

License

MIT

Transports

stdio

Author

RJW34

Submitted by

RJW34

About Weather Edge MCP Server

MCP server for calibrated weather prediction market signals (Kalshi). Dual-model NWS + GFS ensemble forecasting.

Weather Edge MCP Server config

Add this server to your MCP-compatible client using the configuration below.

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

Weather Edge MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Weather Edge MCP?

Weather Edge MCP is an MCP server that provides calibrated Kalshi weather‑market signals. It combines public National Weather Service forecasts and Kalshi market prices into a compact tool surface for AI agents.

How to use Weather Edge MCP?

Install via pip install weather-edge-mcp. Configure it in Claude Desktop by adding the command python -m weather_edge_mcp to your MCP server list. For other clients, run weather-edge-mcp or python -m weather_edge_mcp with optional transport flags (--transport stdio, --transport sse --port 8050, or --transport streamable-http --port 8050).

Key features of Weather Edge MCP

  • Calibrates NWS daily high‑temperature forecasts by city
  • Reads current Kalshi weather market prices
  • Estimates per‑bucket probability, edge, and net expected value
  • Exposes five MCP tools for AI agent consumption
  • Ships an optional FastAPI web app with health and dashboard routes
  • Supports multiple transports: stdio, SSE, and streamable HTTP

Use cases of Weather Edge MCP

  • An AI agent can fetch calibrated weather signals for a single city before making a market decision.
  • A dashboard can query all supported cities at once to view market opportunities.
  • Developers can retrieve bias‑adjusted forecast context to inform custom analyses.
  • Automated trading bots can integrate real‑time METAR observations and Kalshi prices.

Frequently asked questions

1

What are the supported cities?

Weather Edge MCP currently supports five cities: nyc, chicago, denver, miami, and la.

2

How is Weather Edge MCP installed?

Install it with `pip install weather-edge-mcp`. No additional configuration is required for basic use.

3

What transport options are available?

The server can be run in stdio mode (default), SSE mode (`--transport sse --port 8050`), or streamable HTTP mode (`--transport streamable-http --port 8050`). The stdio mode stays clean and side‑effect free.

4

Does Weather Edge MCP require authentication?

MCP stdio mode requires no authentication. The optional FastAPI web app can be gated with the x402 stack if that module is installed and configured.

5

What data sources does Weather Edge MCP use?

It uses the National Weather Service forecast API, the Aviation Weather METAR API, and the Kalshi public market API. No data is stored locally.

Comments

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