Planetary Computer MCP Server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Planetary Computer MCP Servershow me sentinel-2 imagery of San Francisco"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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
Related MCP server: CMR Model Context Protocol
Features
Unified Interface: Single
download_datatool that automatically detects datasets from natural language queriesNatural 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 syncUsage
As MCP Server
python -m planetary_computer_mcp.serverDirect 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 nametime_range: ISO8601 datetime rangemax_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 namelimit: 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 --devTesting
uv run pytestLinting/Formatting
uv run pre-commit run --all-filesArchitecture
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 pointLicense
Apache 2.0 License
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