stac-eo-mcp

An MCP server that lets AI agents (Claude Desktop, Claude Code, Cursor) search satellite imagery in STAC catalogs and measure vegetation, water and burn indices on Sentinel-2 scenes, without downloading whole images.

Ask "How green was Villa Borghese in Rome during 2025?" and the agent finds the scenes, reads only the pixels inside your area, masks clouds and answers with numbers, a map and a trend.

Features

• Two public catalogs: Microsoft Planetary Computer (assets signed automatically) and Element84 Earth Search. No account or API key needed.

• Windowed reads: only the bbox is read from Cloud-Optimized GeoTIFFs with odc-stac, at 10 m in the scene's UTM grid.

• Correct reflectance: applies the Sentinel-2 processing baseline 04.00 offset (−1000 DN), from asset metadata when present, otherwise from the baseline. Both catalogs give identical numbers for the same scene.

• Cloud masking with SCL, reported as valid_pixel_pct. Tile cloud cover often differs from your area (a "9.7% cloudy" tile can be 100% cloudy over your bbox), so this matters.

• LLM-friendly output: statistics, dates and file paths only; never raster arrays. Footprints are simplified. Errors say what went wrong and what to try next.

• Safe by default: area and scene-count limits stop accidental large downloads.

Related MCP server: groundstation

Tools

Resources: stac://indices (formulas, bands, interpretation, cloud mask classes) and stac://catalogs (catalogs, defaults and limits). Prompt: analyze_vegetation(bbox, months=6), a guided vegetation analysis.

Conventions: bbox is [west, south, east, north] in WGS84 degrees; date_range is "YYYY-MM-DD/YYYY-MM-DD".

Install

Requires uv. The server runs locally over stdio; nothing to deploy.

uvx stac-eo-mcp   # runs the server (it waits for an MCP client on stdin)

Claude Desktop

Add to claude_desktop_config.json (Settings → Developer → Edit Config):

{
  "mcpServers": {
    "stac-eo-mcp": {
      "command": "uvx",
      "args": ["stac-eo-mcp"],
      "env": {
        "STAC_EO_MCP_OUTPUT_DIR": "/Users/you/stac-eo-mcp-output"
      }
    }
  }
}

If Claude Desktop cannot find uvx, use its full path (which uvx).

Claude Code

claude mcp add stac-eo-mcp -- uvx stac-eo-mcp
# with options:
claude mcp add stac-eo-mcp -e STAC_EO_MCP_CATALOG=earth-search -- uvx stac-eo-mcp

Cursor

.cursor/mcp.json:

{
  "mcpServers": {
    "stac-eo-mcp": { "command": "uvx", "args": ["stac-eo-mcp"] }
  }
}

Configuration

All optional, via environment variables:

Example conversation

You: How did vegetation in Villa Borghese, Rome (bbox 12.47, 41.905, 12.50, 41.925) change during 2025?

Claude calls time_series(index="ndvi", bbox=[12.47, 41.905, 12.5, 41.925], date_range="2025-01-01/2025-12-31")

Claude: I analysed 24 of the 51 Sentinel-2 dates in 2025 (sampled evenly); 22 had enough clear pixels. Mean NDVI stays between 0.28 and 0.39, typical of a park mixed with city blocks:

Date	Mean NDVI
2025-01-04	0.37
2025-05-01	0.35
2025-07-03	0.28 (minimum)
2025-09-28	0.35
2025-11-05	0.39 (maximum)

The summer dip and autumn recovery is the usual Mediterranean pattern: dry-season stress in July, regrowth after the autumn rains. 10 July and 16 October were skipped (fully cloudy over the park even though the tiles reported under 30% cloud).

You: Show me the map for the end of July.

Claude calls search_scenes(...), then compute_index(item_id="S2A_MSIL2A_20250730T100051_R122_T33TTG_20250730T153414", index="ndvi", bbox=[...], save="png")

Claude: 30 July is 91% clear, mean NDVI 0.32 (max 0.90 on the park's tree cover, negative on the Tiber). Map saved to ~/stac-eo-mcp-output/S2A_MSIL2A_20250730T…_ndvi_c75d9e23.png.

Other questions that work well:

• "Find Sentinel-2 scenes under 10% cloud over Lake Trasimeno in August 2025 and compute NDWI on the clearest one."

• "Compare NBR before and after a wildfire": two compute_index calls; dNBR = NBRpre − NBRpost.

• "Which collections does Earth Search have?"

How it works

agent ──MCP/stdio──▶ server.py ──▶ analysis.py ──▶ catalog.py  (pystac-client: search, signing, band names)
                                         │
                                         ├──────▶ raster.py   (odc-stac window read, PNG/GeoTIFF)
                                         └──────▶ indices.py  (numpy: reflectance, SCL mask, stats)

• models.py validates inputs (WGS84 bbox, ISO dates, limits) and defines compact outputs.

• indices.py is pure numpy and fully unit-tested on synthetic arrays.

• Blocking I/O runs in worker threads; time_series reads up to 4 scenes in parallel.

• Where two Sentinel-2 tiles overlap, time_series keeps one scene per day (best bbox coverage, then lowest cloud cover).

Development

git clone https://github.com/bbrauzzi/stac-eo-mcp && cd stac-eo-mcp
uv sync
uv run pytest            # fast tests (no network)
uv run pytest -m slow    # live tests against both catalogs (~1-2 min)
uv run ruff check . && uv run ruff format --check .
npx @modelcontextprotocol/inspector -e STAC_EO_MCP_OUTPUT_DIR=/tmp/out uv run stac-eo-mcp  # MCP Inspector (pass env with -e)
uv run python scripts/make_readme_figures.py             # regenerate the README figures

Limitations

• Indices are Sentinel-2 L2A only; search and details work on any collection.

• Bounding boxes crossing the antimeridian are not supported.

• The mask keeps snow/ice pixels (SCL class 11); exclude snowy periods for vegetation work.

License

MIT. Sentinel-2 data: Copernicus Sentinel data, processed by ESA.