Space Weather Data MCP Server

Space Weather Data MCP Server

Provides AI assistants with access to real-time space weather data and forecasts from NOAA's Space Weather Prediction Center, enabling queries and interpretations of geomagnetic storms, solar flares, and related indices.

Category
访问服务器

README

Space Weather Data MCP Server

An MCP (Model Context Protocol) server that gives AI assistants access to real-time space weather data and forecasts from NOAA's Space Weather Prediction Center (SWPC).

What It Does

This server exposes NOAA's SWPC data services to any MCP-compatible AI client. The assistant can browse the data directory, fetch live observations and forecasts, look up product definitions, and interpret values using built-in space weather knowledge — all without leaving the conversation.

Tools (callable by the AI on demand):

Tool Description
list_known_paths Curated map of the SWPC data server layout and notable files
list_directory Browse any directory on the SWPC data server
fetch_file Fetch a JSON or text data file and return its contents
get_space_weather_scales NOAA G/S/R storm scales and solar flare A–X classification
list_products Catalog of all SWPC products with descriptions and data file paths
describe_product Fetch the full description of any SWPC product from the NOAA website
get_data_file_info Field definitions and interpretation guide for key JSON data files

Prompts (pre-built context bundles):

Prompt Description
get_forecast Fetches the 3-day forecast, geomagnetic forecast, forecast discussion, SGARF, and advisory outlook — combined into one block
get_kp_index Fetches the NOAA Planetary K-index (3-hour) and daily geomagnetic indices
get_dst_index Fetches the Kyoto Dst index (hourly) with an interpretation guide prepended

Data Source

All data comes from NOAA SWPC's public data server:

  • Data: https://services.swpc.noaa.gov (JSON + text files, no API key required)
  • Product info: https://www.swpc.noaa.gov/products-and-data

No authentication is required. Data is provided by NOAA as a public service.

Requirements

  • Python 3.14+
  • uv (recommended) or pip

Installation

git clone https://github.com/JimFlannery/space-weather-data-mcp.git
cd space-weather-data-mcp
uv sync

Configuration

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "spaceweather": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/absolute/path/to/space-weather-data-mcp",
        "main.py"
      ]
    }
  }
}

Config file locations:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Windows: %APPDATA%\Claude\claude_desktop_config.json

VS Code (Copilot / MCP extension)

Add to your VS Code settings.json:

{
  "mcp.servers": {
    "spaceweather": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/absolute/path/to/space-weather-data-mcp",
        "main.py"
      ]
    }
  }
}

Development / Testing

Run the MCP Inspector to test tools and prompts interactively:

uv run mcp dev main.py

Example Queries

Once connected to an MCP client, you can ask things like:

  • "What is the current space weather forecast?"
  • "Is there a geomagnetic storm in progress? Check the Kp index."
  • "What are today's active solar regions and their flare probabilities?"
  • "Fetch the latest GOES X-ray flux and classify any solar flares."
  • "What does a Kp of 7 mean for power grids?"
  • "Show me the Dst index and explain whether we're in a storm's main phase or recovery phase."

Space Weather Scales Quick Reference

Scale Measures Range Storm starts at
G (Geomagnetic) Kp index G1–G5 G1 (Kp = 5)
S (Solar Radiation) ≥10 MeV proton flux S1–S5 S1 (10 pfu)
R (Radio Blackout) X-ray flux (0.1–0.8 nm) R1–R5 R1 (M1 flare)

Solar flares: A → B → C → M → X (each class is 10× stronger; X-class is open-ended).

Use the get_space_weather_scales tool for the full threshold and effects table.

License

MIT — see LICENSE.

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选