dynamical.org Weather & Climate Catalog MCP Server

Get 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 storage format or location changes. This tool also returns the dataset's low-level storage details (from the STAC asset, fetched live) and a lower-level xarray/fsspec snippet for callers who need direct access instead of the wrapper package. Args: collection_id: A STAC collection id, e.g. "noaa-gfs-forecast". Use search_catalog to discover ids. Returns: A dict with the recommended `dynamical_catalog.open(...)` snippet, a `worked_example` pulled from the collection's own STAC metadata when one is published, the raw asset URI/type/storage options, and a generated low-level open snippet (icechunk/zarr/geoparquet, chosen from the asset's declared type). Raises ValueError (listing valid ids) if collection_id is unknown.

Search 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 region ("continental US", "global"), or a resolution ("3km", "0.25 degree"). Results are ranked by number of matching terms. Args: query: Free-text search terms, e.g. "hourly precipitation CONUS" or "ECMWF ensemble forecast". limit: Maximum number of results to return (default 5). Returns: {"query": ..., "results": [{"collection_id", "title", "model_name", "description_summary", "spatial_domain", "spatial_resolution", "matched_variables", "score"}, ...]}, most relevant first. Pass a result's collection_id to get_dataset_info, get_access_pattern, or list_recent_runs.

Get 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 -- it's always as fresh as the STAC catalog itself. Args: collection_id: A STAC collection id, e.g. "noaa-gfs-forecast", "noaa-hrrr-analysis", or "ecmwf-aifs-ens-forecast". Use search_catalog to discover ids. Returns: A dict with title/model name, prose descriptions, spatial and time domain/resolution, forecast range (for forecast datasets), license and attribution, the dataset's variables, and links to its docs page and example notebooks. Raises ValueError (listing valid ids) if collection_id is unknown.

Check 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 by this feed). Args: collection_id: A STAC collection id, e.g. "noaa-gfs-forecast". limit: Maximum number of recent runs to return, most recent first (default 10). Returns: A dict with the overall pipeline `sla_status`, this product's cadence and next expected init/completion time, typical latency stats, and up to `limit` recent runs (`init_time`, `status`, `completion_pct`, `on_timedness`, arrival/latency timestamps). If collection_id isn't pipeline-monitored, returns `monitored`: False plus the list of collection ids that are.