What is Jupyter Earth Mcp Server

Jupyter Earth MCP Server is an implementation of the Model Context Protocol (MCP) that provides tools for geospatial analysis within Jupyter notebooks.

Use cases

Use cases include analyzing sea level rise, visualizing climate change impacts, and conducting spatial data analysis for research and policy-making.

How to use

To use Jupyter Earth MCP Server, install the necessary packages including jupyterlab, jupyter-collaboration, and ipykernel. Then, start JupyterLab to begin working with geospatial datasets.

Key features

Key features include integration with Earthdata for dataset searching, real-time collaboration capabilities, and tools for advanced geospatial analysis.

Where to use

Jupyter Earth MCP Server is used in fields such as environmental science, climate research, urban planning, and any domain requiring geospatial data analysis.

🌎 ✨ Jupyter Earth MCP Server

🌍 Jupyter Earth MCP Server is a Model Context Protocol (MCP) server implementation that provides a set of tools for 🗺️ Geospatial analysis in 📓 Jupyter notebooks.

The following demo uses the Earthdata MCP server to search for datasets and data granules on NASA Earthdata, this MCP server to download the data in Jupyter and the jupyter-mcp-server to run further analysis.

Start JupyterLab

Make sure you have the following installed. The collaboration package is needed as the modifications made on the notebook can be seen thanks to Jupyter Real Time Collaboration.

pip install jupyterlab==4.4.1 jupyter-collaboration==4.0.2 ipykernel
pip uninstall -y pycrdt datalayer_pycrdt
pip install datalayer_pycrdt==0.12.17

Then, start JupyterLab with the following command.

jupyter lab --port 8888 --IdentityProvider.token MY_TOKEN --ip 0.0.0.0

You can also run make jupyterlab.

[!NOTE]

The --ip is set to 0.0.0.0 to allow the MCP server running in a Docker container to access your local JupyterLab.

Use with Claude Desktop

Claude Desktop can be downloaded from this page for macOS and Windows.

For Linux, we had success using this UNOFFICIAL build script based on nix

# ⚠️ UNOFFICIAL
# You can also run `make claude-linux`
NIXPKGS_ALLOW_UNFREE=1 nix run github:k3d3/claude-desktop-linux-flake \
  --impure \
  --extra-experimental-features flakes \
  --extra-experimental-features nix-command

To use this with Claude Desktop, add the following to your claude_desktop_config.json (read more on the MCP documentation website).

[!IMPORTANT]

Ensure the port of the SERVER_URLand TOKEN match those used in the jupyter lab command.

The NOTEBOOK_PATH should be relative to the directory where JupyterLab was started.

Claude Configuration on macOS and Windows

{
  "mcpServers": {
    "jupyter-earth": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "SERVER_URL",
        "-e",
        "TOKEN",
        "-e",
        "NOTEBOOK_PATH",
        "datalayer/jupyter-earth-mcp-server:latest"
      ],
      "env": {
        "SERVER_URL": "http://host.docker.internal:8888",
        "TOKEN": "MY_TOKEN",
        "NOTEBOOK_PATH": "notebook.ipynb"
      }
    }
  }
}

Claude Configuration on Linux

CLAUDE_CONFIG=${HOME}/.config/Claude/claude_desktop_config.json
cat <<EOF > $CLAUDE_CONFIG
{
  "mcpServers": {
    "jupyter-earth": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "SERVER_URL",
        "-e",
        "TOKEN",
        "-e",
        "NOTEBOOK_PATH",
        "--network=host",
        "datalayer/jupyter-earth-mcp-server:latest"
      ],
      "env": {
        "SERVER_URL": "http://localhost:8888",
        "TOKEN": "MY_TOKEN",
        "NOTEBOOK_PATH": "notebook.ipynb"
      }
    }
  }
}
EOF
cat $CLAUDE_CONFIG

Components

Tools

The server currently offers 1 tool:

1. download_earth_data_granules

• Add a code cell in a Jupyter notebook to download Earth data granules from NASA Earth Data.
• Input:
  • folder_name(string): Local folder name to save the data.
  • short_name(string): 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).
• Returns: Cell output.

Prompts

1. download_analyze_global_sea_level
   • To ask for downloading and analyzing global sea level data in Jupyter.
   • Returns: Prompt correctly formatted.

Building

You can build the Docker image it from source.

make build-docker