Earthdata MCP Server provides a bridge between AI assistants and NASA's Earthdata platform, enabling search and retrieval of Earth science datasets and data granules. Built with FastMCP, it exposes two main tools: search_earth_datasets for discovering datasets based on keywords, temporal ranges, and geographic boundaries, and search_earth_datagranules for retrieving specific data granules by dataset short name. This implementation is particularly valuable for researchers, scientists, and analysts who need to access NASA Earth observation data directly through conversational AI interfaces without switching to specialized data portals.
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Highlight AISearch for datasets on NASA Earthdata. Input: search_keywords (str), count (int), temporal (tuple, optional), bounding_box (tuple, optional). Returns: List of dataset abstracts.
Search for data granules on NASA Earthdata. Input: short_name (str), count (int), temporal (tuple, optional), bounding_box (tuple, optional). Returns: List of data granules.
Download Earth data granules from NASA Earth Data and integrate with Jupyter notebooks. Input: folder_name (str), short_name (str), count (int), temporal (tuple, optional), bounding_box (tuple, optional). Returns: Success message with download code preparation details.
Add markdown cells to notebooks.
Insert markdown cells at specific positions.
Modify existing cell content.
Add and execute code cells.
Insert and execute code cells at specific positions.
Execute cells with progress monitoring.
Execute cells with timeout.
Execute cells with streaming output.
Read all notebook cells.
Read specific notebook cells.
Get notebook metadata.
Delete notebook cells.
