Copernicus

A self-hosted Model Context Protocol server that gives MCP-compatible LLM agents local, reproducible access to Copernicus environmental data — observations, reanalysis, forecasts, and climate indicators.

Two backends are currently supported, both using Copernicus services that are free to register for: Copernicus Marine, CDS, ADS, and EWDS.

• Copernicus Marine (CMEMS) — 1,251 datasets across 306 products in the bundled catalogue snapshot: physics, biogeochemistry, sea ice, ocean colour, SST, sea level, waves, wind, and in-situ observations. Supports discovery, subsetting, native-file retrieval, and sync or async downloads.
• Climate Data Store family (CDS / ADS / EWDS) — 164 datasets in the bundled snapshot across reanalysis, satellite, in-situ, atmospheric composition (CAMS), and emergency-management (EFAS / GloFAS / CEMS) data. Uses queue-based asynchronous retrieval with offline discovery from a bundled catalogue snapshot. A request that exceeds a dataset's server-side cost limit is split automatically along the calendar axis and returned as one multi-file workflow under a single request id.

Ask in plain English. The server finds, filters, estimates, and downloads. Large downloads are size-estimated, gated for explicit confirmation, cached, and returned as a filepath + metadata + provenance descriptor rather than inline bytes. Every retrieval lands with an MD5-sealed sidecar JSON so the exact request is reproducible later. Long-running requests run asynchronously — submit one and poll it later, or list and reclaim your past jobs from a fresh session, because the local job store survives restarts.

"Get me Mediterranean Sea salinity forecasts for next week, then fetch the AMOC strength time series for the last 5 years."

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Install

No hosted endpoint. No vendor account. No data upload. Python 3.11+ required.

With venv (stdlib, no extra tools)

python -m venv .venv
source .venv/bin/activate           # macOS / Linux
# .venv\Scripts\activate             # Windows

pip install "copernicus-mcp[cmems,cds]"     # both backends (recommended)
# pip install "copernicus-mcp[cmems]"       # CMEMS only
# pip install "copernicus-mcp[cds]"         # CDS / ADS / EWDS only

With conda / mamba / micromamba

The package is currently published on PyPI only (no conda-forge feedstock yet) — so pip install inside a fresh conda environment is the path:

mamba create -n copernicus python=3.11 pip       # or `conda create ...`
mamba activate copernicus
pip install "copernicus-mcp[cmems,cds]"

The MCP client config (next section) then points at /path/to/your/conda/envs/copernicus/bin/copernicus-mcp instead of the venv path. Run which copernicus-mcp inside the activated environment to find the exact location.

Credentials

The Copernicus services own the credentials — we never see them.

CMEMS (free account at https://data.marine.copernicus.eu/register):

copernicusmarine login            # writes ~/.copernicusmarine/.copernicusmarine-credentials

CDS / ADS / EWDS — a single Personal Access Token works across all three stores under ECMWF's unified-token policy. Get it at https://cds.climate.copernicus.eu/ → user profile:

export CDSAPI_KEY=<your-uuid-pat>
# or write to ~/.cdsapirc (same format the cdsapi CLI uses)

Some CDS-family datasets require accepting their licence once; when that happens the server returns the acceptance URL in a structured TermsNotAcceptedError.

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Configure your MCP client

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or the equivalent on your platform:

{
  "mcpServers": {
    "copernicus": {
      "command": "/absolute/path/to/.venv/bin/copernicus-mcp",
      "args": ["serve"]
    }
  }
}

Restart Claude Desktop. Tools become available the next time you open a chat.

Claude Code

claude mcp add copernicus -- /absolute/path/to/.venv/bin/copernicus-mcp serve

Other MCP clients

Any client that speaks MCP over stdio works. Point it at the copernicus-mcp serve command in your virtualenv.

Smoke test

After install, confirm the server starts and your credentials resolve:

copernicus-mcp status

The output lists configured backends and where credentials were resolved from — without ever printing the credential values themselves.

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Try these prompts

Drop these into a chat with the server connected. Each one walks through the discovery → estimate → download flow and lands a real file on your disk.

#	Prompt	What the agent does
1	"Mediterranean salinity forecast for the next 7 days."	Routes to the physics-mediterranean-state group → picks MEDSEA_ANALYSISFORECAST_PHY_006_013 → downloads a daily-mean NetCDF subset.
2	"How has Arctic sea-ice extent changed over the last 5 years?"	Routes to the sea-ice-arctic group → finds ARCTIC_OMI_SI_extent indicator timeseries + SEAICE_ARC_PHY_AUTO_L3_MYNRT_011_023 satellite L3.
3	"Get me the AMOC strength timeseries at 26°N."	Intent-heavy query — routes to ocean-monitoring-indicators → returns GLOBAL_OMI_NATLANTIC_amoc_26N_profile and _amoc_max26N_timeseries.
4	"ERA5 hourly 2m temperature over Europe for January 2024."	CDS path → describes reanalysis-era5-single-levels → submits a request, polls until the queue settles, lands a GRIB / NetCDF file.
5	"Global CO₂ atmospheric forecasts for next week."	ADS path → finds cams-global-greenhouse-gas-forecasts → submits + waits + downloads.

The discovery routing is covered offline by bench/marine_routing_bench.py; the full submit-download flows are covered by tests/integration/ and require live CMEMS / CDS credentials with RUN_INTEGRATION_TESTS=1.

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Tools

Backend	Tool	Purpose
diagnostic	copernicus_mcp_status	Configured backends, cache size, override hints. No credentials in output.
	copernicus_mcp_list_jobs	Recover past jobs across sessions: list recent submissions (id, backend, dataset, status) from local state — no request_id needed after a restart.
CMEMS	marine_search_groups → marine_search_products → marine_search_datasets	Hierarchical discovery — narrows ~1251 datasets in two hops via 47 hand-curated routing groups. Offline, no embeddings, no LLM at query time.
	marine_describe_dataset	Full metadata: variables, axes, spatial / temporal extent, services, DOI.
	marine_get_coordinates	The dataset's actual lon/lat/depth/time axes — summarised for long axes.
	marine_estimate_subset	Preview the download size before running it — an approximate estimate, not a guarantee.
	marine_subset_dataset	Download a spatio-temporal subset. Large requests require explicit confirmation. async_mode=true returns immediately.
	marine_list_files → marine_get_files	For sparse / in-situ datasets (CORA, EasyCORA, INSITU-BGC, MULTIOBS): filter by bbox / time / variables, then download the precise file list.
	marine_check_status, marine_cancel_subset	Async lifecycle.
CDS / ADS / EWDS	cds_search_groups → cds_search_datasets	Hierarchical group discovery + filters (bbox / time_range / variable / domain / category).
	cds_describe_dataset, cds_apply_constraints	Bundled snapshot + live narrowing against the store's constraints endpoint.
	cds_estimate_request	Self-calibrating size estimate + costing pre-flight (flags requests the server will reject); honest "unknown" for whole-file products. The byte size is approximate (see note below).
	cds_apply_constraints	Valid field values for a (partial) request; anonymous.
	cds_submit_request, cds_check_request_status, cds_download_request_result, cds_cancel_request	Async queue lifecycle. T&C-not-accepted surfaces as a structured error with the accept-URL.

Tools that return large data return {filepath, uri, metadata, provenance} — never inline bytes. The copernicus://files/{cache_key}, copernicus://jobs/{request_id}, and copernicus://provenance/{record_id} resources surface the cached artifacts to MCP clients that prefer the resource API.

Size estimates are approximate. The byte size from cds_estimate_request / marine_estimate_subset (and the size shown at the confirmation gate) is a heuristic or calibration-based figure and can be wrong by a large factor — treat it as a rough order-of-magnitude, not a guarantee. Don't rely on it for hard limits, quotas, billing, or disk provisioning. The CDS server cost units are exact; only the byte size is uncertain. Every CDS estimate response also carries a size_estimate_caveat field restating this.

For complete schemas read the inline tool descriptions your MCP client surfaces, or the detailed reference in docs/tools.md.

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How it works

   ┌──────────────────────────┐       ┌─────────────────────────────┐
   │  Claude / Claude Code /  │       │   copernicus-mcp serve      │
   │  any MCP client          │ stdio │   (local Python process)    │
   └──────────────────────────┘ ────▶ │                             │
                                      │  hierarchical discovery     │
                                      │  size estimate + gate       │
                                      │  retrieval + provenance     │
                                      │  cache + idempotent re-use  │
                                      └──────────────┬──────────────┘
                                                     │
                          ┌──────────────────────────┼────────────────────────┐
                          ▼                          ▼                        ▼
                  Copernicus Marine          Climate Data Store          local cache
                  (Mercator Ocean)           CDS / ADS / EWDS            ~/Library/Caches/...
                  copernicusmarine SDK       cdsapi SDK                  (per-OS paths)

Hierarchical discovery uses bundled JSON manifests (slim records → enriched cards → product summaries → routing groups). Scoring is deterministic phrase matching. The catalogue + groups are committed JSON, so "why did this query return that group" is auditable.

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Limitations

• The server does not bypass Copernicus licences or access controls — credentials and licence acceptance remain the user's responsibility.
• CDS-family downloads depend on upstream queue availability; large requests may take minutes to hours.
• Catalogue snapshots are bundled at release time and may lag behind the live Copernicus catalogues. Re-publish a new version when the snapshots are refreshed.
• Hierarchical routing is pattern-based; it does not use embeddings or an LLM at query time. Misroutes on highly ambiguous queries are possible.

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Configuration

The system runs out of the box. Override via env vars (COPERNICUS_MCP_CACHE_DIR, COPERNICUS_MCP_LOG_LEVEL, COPERNICUS_MCP_ENABLED_BACKENDS=cmems,cds), a YAML file at ~/.config/copernicus-mcp/config.yaml, or --cache-dir PATH on the entry-point binary.

Cache directories are per-OS via platformdirs: Linux ~/.cache/copernicus-mcp/, macOS ~/Library/Caches/copernicus-mcp/, Windows %LOCALAPPDATA%\copernicus-mcp\Cache\.

Download location is fixed at startup. To change where files are written, set COPERNICUS_MCP_CACHE_DIR (or --cache-dir / config.yaml) before launching the server — there is no per-request override and it cannot be changed while the server is running. Decide this before pointing an MCP client (or agent) at the server; changing it later requires a restart.

Full reference: docs/setup.md.

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Status

Latest release: see releases page (current: v0.4.3). Two backends in production: CMEMS + Climate Data Store family. CDSE, Sentinel Hub, WEkEO planned for subsequent iterations.

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License

BSD 3-Clause. See LICENSE. Dependencies are EUPL-1.2 (copernicusmarine), Apache-2.0 (cdsapi and most others), MIT or BSD.

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Acknowledgements

• Mercator Ocean International for copernicusmarine.
• ECMWF for cdsapi and for operating the Climate / Atmosphere / Early Warning data stores.
• The Copernicus programme for the underlying data.
• The Anthropic team for the Model Context Protocol specification and Python SDK.

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Related projects

• AQUAVIEW MCP — hosted MCP server unifying ~700K datasets across 68 NOAA / IOOS / EMODnet / Argo / GOES-R / Sentinel collections. If your question reaches beyond Copernicus into US / global multi-agency oceanographic and atmospheric data, that's the natural complement to this server.