Planetary Computer MCP Server
Related Servers
Alternatives to Planetary Computer MCP Server
No user-submitted related servers found.
Related Servers
- FlicenseBqualityBmaintenanceA Python MCP server for discovering and analyzing Sentinel-2 L2A imagery, enabling AI agents to search scenes, check quality, and compute spectral indices like NDVI, NDWI, and NBR from remote COG windows.4-
- AlicenseNot gradedqualityAmaintenanceAn MCP server that connects AI agents to cloud-native geospatial data via STAC metadata and DuckDB with H3 spatial indexing, enabling zero-configuration SQL queries on terabyte-scale datasets over S3.23BSD 3-Clause
- AlicenseNot gradedqualityDmaintenanceMCP server for interacting with Google Earth Engine, enabling geospatial analysis such as dataset visualization, statistics computation, and search via AI assistants.15MIT
- AlicenseNot gradedqualityCmaintenanceA Python-based MCP server that provides access to Ordnance Survey APIs, allowing querying of geographic data through a standardized protocol with features like collection management, feature search, and spatial filtering.2MIT
- AlicenseCqualityDmaintenanceMCP server for the Geopera geospatial data platform that enables AI agents to discover imagery, place and manage orders, and run analytics using the same API as other Geopera clients.100MIT
- AlicenseNot gradedqualityAmaintenanceAn MCP server for Earth Observation processing, exposing Rust-accelerated EO computation tools like spectral indices, change detection, and classification for AI agents.Apache 2.0
TDQS
Scored across 2 tools
Both tools have clearly distinct purposes: one for raster satellite data with natural language querying, and one for vector geometries with a collection ID. No overlap in functionality.
Both tools follow a consistent download_<type>_tool pattern using snake_case, making the naming predictable and clear.
With only 2 tools, the server feels very thin for the broad scope of Microsoft Planetary Computer. Even a minimal interface would benefit from additional tools like listing collections or searching items.
The server covers raster and vector data downloads but lacks critical supporting operations such as querying available collections, filtering items, or exploring data metadata. Users must know collection IDs or rely on natural language detection without discovery tools.