Server data from the Official MCP Registry
US NWS alerts by state and short-term (next 5 periods) point forecasts.
US NWS alerts by state and short-term (next 5 periods) point forecasts.
Valid MCP server (1 strong, 4 medium validity signals). No known CVEs in dependencies. Imported from the Official MCP Registry.
4 files analyzed · No issues found
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From the project's GitHub README.
An MCP server that provides:
Data is sourced from the public NWS API at https://api.weather.gov.
| Tool | Description | Inputs |
|---|---|---|
get_alerts | Fetch active NWS alerts for a US state | state (string, two-letter code) |
get_forecast | Fetch short-term forecast (next 5 periods) for coordinates | latitude (number), longitude (number) |
Install dependencies:
pip install .
Run the MCP server (stdio transport):
python -m weather.weather
Your MCP client should be configured to launch the server via the package entrypoint or the above module path.
See server.json for registry metadata including name, version, tools, and entrypoint configuration.
mcp-publisher login github
mcp-publisher publish
curl "https://registry.modelcontextprotocol.io/v0/servers?search=io.github.vtiwari/weather-mcp"
If this directory is not yet a git repo:
git init
git add .
git commit -m "Initial commit: Weather MCP server"
Create repo (GitHub CLI) and push:
gh repo create vtiwari/weather-mcp --public --source . --remote origin --push
If not using GitHub CLI, create the repo manually via the GitHub web UI, then:
git remote add origin https://github.com/vtiwari/weather-mcp.git
git branch -M main
git push -u origin main
Tag version for release consistency:
git tag v0.1.0
git push origin v0.1.0
To distribute via PyPI, ensure pyproject.toml includes build backend and metadata (authors, license). Then:
pip install build twine
python -m build
twine upload dist/*
MIT (adjust if different).
This server uses public NOAA/NWS endpoints. Respect API usage guidelines and rate limits.
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