Nephyr Weather
Pull GFS ensemble weather signals across 12 cities to spot prediction-market mispricings from your agent.
Overview
Nephyr Weather is an MCP server for the Idea phase that delivers GFS ensemble weather signals across 12 cities for prediction-market edge detection.
What is this MCP server?
- GFS ensemble weather signals for market edge detection
- Coverage across 12 cities for comparable forecasts
- Designed for prediction-market pricing discrepancies
- stdio MCP server v0.1.1 via uvx on PyPI
- Fits Nephyr stack with wallets and risk servers
- 12 cities in scope per catalog description
- GFS ensemble signal source
- Server version 0.1.1, stdio via uvx on PyPI
What problem does it solve?
Weather prediction markets move on forecast noise you cannot quickly compare to ensemble data inside your dev workflow.
Who is it for?
Indie builders focusing on weather-linked prediction markets who want ensemble context in MCP chat.
Skip if: Builders who need global aviation weather, long-range climate models, or non-market consumer forecasts only.
What do I get? / Deliverables
Your agent contrasts GFS ensemble signals with market prices so you can shortlist mispriced city contracts faster.
- Ensemble-based weather signals for supported cities
- Agent-ready comparisons for market research notes
- Inputs for alert thresholds or semi-auto trading rules
Recommended MCP Servers
Journey fit
How it compares
Specialized forecast-signal MCP for markets, not a general OpenWeather-style app integration.
Common Questions / FAQ
Who is Nephyr Weather for?
Solo developers and traders who use coding agents to research weather-related prediction markets with GFS ensemble data.
When should I use Nephyr Weather?
Use it when scouting city-level weather markets, validating a prototype alert, or refreshing ensemble vs. market implied odds.
How do I add Nephyr Weather to my agent?
Configure stdio MCP with the nephyr-weather PyPI package (uvx, v0.1.1) in Claude Code, Cursor, or another MCP client.