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dynamical.org Weather & Climate Catalog
io.github.dynamical-org/mcp · v0.1.1
No registry update 90+ days
Measured by MCP Station
| Last check | Reachable |
|---|---|
| Checked at | — |
| Check success 7 / 30 days | — |
| First-try success 30 days | — |
| Median response | — |
| Lookups 30 days | — |
| Authentication | No auth |
| MCP version | 2025-06-18 |
| Rank score | — |
| Declared risk | Read-only (all tools declare readOnlyHint) |
| Capabilities declared | experimental, prompts, resources, tools |
| Tool definitions | 4 tools · with description 100% · input schema is an object 100% · read/destructive hints 100% |
Description written by the publisher, not verified
Search dynamical.org's open STAC catalog of weather & climate datasets (GFS, ECMWF, HRRR).
Connect
streamable-http · https://mcp.dynamical.org/mcp
claude mcp add --transport http mcp https://mcp.dynamical.org/mcp{
"mcpServers": {
"mcp": {
"url": "https://mcp.dynamical.org/mcp"
}
}
}Tools as reported by the server
get_access_patternGet the storage URI and working code for opening a dynamical.org dataset's data. dynamical.org publishes a Python package, `dynamical-catalog`, that reads the STAC catalog itself to resolve and open a dataset -- it's the recommended access pattern because it can't go stale even if the underlying stoget_dataset_infoGet documentation, spatial/time resolution, domain, and update cadence for one dynamical.org dataset. dynamical.org/catalog is itself rendered from this same STAC catalog, so this tool fetches the collection document live (short TTL cache) rather than relying on anything baked into this server -- itlist_recent_runsCheck run freshness and arrival status for a dynamical.org forecast dataset, from the same public feed status.dynamical.org's dashboard polls. Only forecast collections are pipeline-monitored today (analysis collections like noaa-gfs-analysis or noaa-mrms-conus-analysis-hourly aren't yet tracked bysearch_catalogSearch dynamical.org's STAC catalog of cloud-optimized weather and climate datasets. Matches against each dataset's model name, description, spatial/time domain and resolution, forecast range, and variable names -- so a query can be a model ("GFS"), a variable ("precipitation", "temperature_2m"), a
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