Planetary Computer MCP Server

A Python implementation of the Planetary Computer MCP server, providing unified access to satellite and geospatial data through natural language queries.

Sample Outputs

<table> <tr> <td align="center"><img src="https://raw.githubusercontent.com/isaaccorley/planetary-computer-mcp/main/assets/images/sentinel_2_l2a_alps.jpg" width="200"><br>_{<b>Sentinel-2</b><br>Alps}</td> <td align="center"><img src="https://raw.githubusercontent.com/isaaccorley/planetary-computer-mcp/main/assets/images/sentinel_2_l2a_coastal-miami.jpg" width="200"><br>_{<b>Sentinel-2</b><br>Miami}</td> <td align="center"><img src="https://raw.githubusercontent.com/isaaccorley/planetary-computer-mcp/main/assets/images/naip_small-seattle.jpg" width="200"><br>_{<b>NAIP</b><br>Seattle}</td> <td align="center"><img src="https://raw.githubusercontent.com/isaaccorley/planetary-computer-mcp/main/assets/images/naip_medium-la.jpg" width="200"><br>_{<b>NAIP</b><br>Los Angeles}</td> </tr> <tr> <td align="center"><img src="https://raw.githubusercontent.com/isaaccorley/planetary-computer-mcp/main/assets/images/hls2_l30_medium-la.jpg" width="200"><br>_{<b>HLS L30</b><br>Los Angeles}</td> <td align="center"><img src="https://raw.githubusercontent.com/isaaccorley/planetary-computer-mcp/main/assets/images/modis_09A1_061_large-bay.jpg" width="200"><br>_{<b>MODIS</b><br>Bay Area}</td> <td align="center"><img src="https://raw.githubusercontent.com/isaaccorley/planetary-computer-mcp/main/assets/images/sentinel_1_rtc_coastal-miami.jpg" width="200"><br>_{<b>Sentinel-1 SAR</b><br>Miami}</td> <td align="center"><img src="https://raw.githubusercontent.com/isaaccorley/planetary-computer-mcp/main/assets/images/cop_dem_glo_30_coastal-miami.jpg" width="200"><br>_{<b>Copernicus DEM</b><br>Miami}</td> </tr> <tr> <td align="center"><img src="https://raw.githubusercontent.com/isaaccorley/planetary-computer-mcp/main/assets/images/esa_worldcover_alps.png" width="200"><br>_{<b>ESA WorldCover</b><br>Alps}</td> <td align="center"><img src="https://raw.githubusercontent.com/isaaccorley/planetary-computer-mcp/main/assets/images/io_lulc_annual_v02_rural-iowa.png" width="200"><br>_{<b>IO LULC</b><br>Iowa}</td> <td align="center"><img src="https://raw.githubusercontent.com/isaaccorley/planetary-computer-mcp/main/assets/images/ms-buildings.jpg" width="200"><br>_{<b>MS Buildings</b><br>Vector Data}</td> <td align="center"><img src="https://raw.githubusercontent.com/isaaccorley/planetary-computer-mcp/main/assets/images/pet_preview.png" width="200"><br>_{<b>TerraClimate PET</b><br>Zarr Preview}</td> </tr> </table>

<table> <tr> <td align="center"><img src="assets/images/gridmet-heatmap-animation.gif" width="300"><br>_{<b>GridMET Climate Data</b><br>Heatmap Animation}</td> <td align="center"><img src="assets/images/terraclimate-heatmap-animation.gif" width="300"><br>_{<b>TerraClimate Data</b><br>Heatmap Animation}</td> </tr> </table>

Features

• Unified Interface: Single download_data tool that automatically detects datasets from natural language queries
• Natural Language Geocoding: Automatically converts place names (e.g., "San Francisco", "the Alps", "Amazon rainforest") to geospatial bounding box coordinates using the Nominatim geocoding service—no need to manually specify coordinates
• Multi-format Support: Raster (GeoTIFF), Vector (GeoParquet), and Zarr data
• Automatic Visualization: Generate RGB/JPEG previews for LLM analysis
• Fast Downloads: Uses odc-stac for efficient COG access

Installation

uv sync

Usage

As MCP Server

python -m planetary_computer_mcp.server

Direct API Usage

from planetary_computer_mcp.tools.download_data import download_data

# Download Sentinel-2 data for San Francisco
result = download_data(
    query="sentinel-2 imagery",
    aoi="San Francisco",
    time_range="2024-01-01/2024-01-31"
)

print(f"Raw data: {result['raw']}")
print(f"Visualization: {result['visualization']}")

Tools

download_data

Unified tool for raster, DEM, land cover, and climate data.

Parameters:

• query: Natural language query (e.g., "sentinel-2", "elevation data")
• aoi: Bounding box [W,S,E,N] or place name
• time_range: ISO8601 datetime range
• max_cloud_cover: Maximum cloud cover (optical data)

Returns:

• Raw GeoTIFF/Zarr/Parquet file
• RGB/JPEG visualization
• Metadata

download_geometries

Tool for vector/building data.

Parameters:

• collection: Collection ID (e.g., "ms-buildings")
• aoi: Bounding box or place name
• limit: Maximum features

Returns:

• GeoParquet file
• Map visualization
• Feature count

Supported Datasets

See collections.md for the complete list of supported datasets.

Development

Setup

uv sync --dev

Testing

uv run pytest

Linting/Formatting

uv run pre-commit run --all-files

Architecture

src/
├── core/           # Core utilities
│   ├── stac_client.py    # STAC search wrapper
│   ├── geocoding.py      # Place name → bbox
│   ├── collections.py    # Dataset metadata
│   ├── raster_utils.py   # odc-stac helpers
│   ├── vector_utils.py   # DuckDB helpers
│   ├── visualization.py  # Matplotlib viz
│   └── zarr_utils.py     # Xarray Zarr helpers
├── tools/          # MCP tools
│   ├── download_data.py
│   └── download_geometries.py
└── server.py       # MCP server entry point

License

Apache 2.0 License