🪐 ✨ Earthdata MCP Server

Earthdata MCP Server is a Model Context Protocol (MCP) server implementation that provides tools to interact with NASA Earth Data.

This server is intentionally Earthdata-only.

If you need notebook/runtime tools, compose this server with `jupyter-mcp-server` using mcp-compose.

Key Features

• Dataset discovery on NASA Earthdata
• Granule search with temporal and bounding box filters
• Flexible download workflow with explicit execution modes

Getting Started

Local install

pip install earthdata-mcp-server

Docker with Claude Desktop

{
  "mcpServers": {
    "earthdata": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "datalayer/earthdata-mcp-server:latest"
      ],
      "env": {
        "EARTHDATA_USERNAME": "your_username",
        "EARTHDATA_PASSWORD": "your_password"
      }
    }
  }
}

Linux host networking

{
  "mcpServers": {
    "earthdata": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "--network=host",
        "datalayer/earthdata-mcp-server:latest"
      ],
      "env": {
        "EARTHDATA_USERNAME": "your_username",
        "EARTHDATA_PASSWORD": "your_password"
      }
    }
  }
}

Tools

The server offers 3 Earthdata tools.

`search_earth_datasets`

• Search for datasets on NASA Earthdata.
• Input:
  • search_keywords (str): Keywords to search for in the dataset titles.
  • count (int): Number of datasets to return.
  • temporal (tuple): (Optional) Temporal range in the format (date_from, date_to).
  • bounding_box (tuple): (Optional) Bounding box in the format (lower_left_lon, lower_left_lat, upper_right_lon, upper_right_lat).
• Returns: List of dataset abstracts.

`search_earth_datagranules`

• Search for data granules on NASA Earthdata.
• Input:
  • short_name (str): Short name of the dataset.
  • count (int): Number of data granules to return.
  • temporal (tuple): (Optional) Temporal range in the format (date_from, date_to).
  • bounding_box (tuple): (Optional) Bounding box in the format (lower_left_lon, lower_left_lat, upper_right_lon, upper_right_lat).
• Returns: List of data granules.

`download_earth_data_granules`

• Search and optionally download granules with explicit modes.
• Authentication: Requires NASA Earthdata Login credentials (see authentication guide)
• Input:
  • folder_name (str): Local folder name to save the data.
  • short_name (str): Short name of the Earth dataset to download.
  • count (int): Number of data granules to download.
  • temporal (tuple): (Optional) Temporal range in the format (date_from, date_to).
  • bounding_box (tuple): (Optional) Bounding box in the format (lower_left_lon, lower_left_lat, upper_right_lon, upper_right_lat).
  • mode (str): One of:
    • `manifest`: Returns granule metadata only.
    • `download`: Downloads files directly on server side.
    • `script`: Returns Python code to execute elsewhere.
  • max_manifest_items (int): Max items returned in `manifest` mode.

How download works

`download_earth_data_granules` always starts by searching for granules with your filters, then behaves based on `mode`:

1. `manifest`

2. `download`

3. `script`

Recommended download strategy

1. Use `mode="manifest"` first to inspect results safely.

2. Use `mode="script"` when you want notebook-driven execution via `mcp-compose` + `jupyter-mcp-server`.

3. Use `mode="download"` only when server-side file writes are intended.

For a full composition example with `mcp-compose`, see download workflow docs.

Prompts

1. `download_analyze_global_sea_level`

2. `sealevel_rise_dataset`

3. `ask_datasets_format`

Building

# or run `docker build -t datalayer/earthdata-mcp-server .`
make build-docker

If you prefer, you can pull the prebuilt images.

make pull-docker