# Jupyter Earth Data

> Use this tool when you need to automate the acquisition of satellite data and integrate it into Jupyter-based analysis workflows for Earth science research. It provides a simple interface for downloading NASA Earth Data granules directly into Jupyter notebooks, allowing for parameterized data retrieval with options for temporal ranges and geographic bounding boxes. This tool is ideal for Earth scientists and researchers who require efficient and seamless data analysis workflows.

Canonical page: https://skillsregistry.net/skills/datalayer-jupyter-earth  
JSON: https://api.skillsregistry.net/v1/skills/datalayer-jupyter-earth

## Description

Jupyter Earth MCP Server provides a bridge between AI assistants and Jupyter notebooks for Earth science data analysis. Developed by Datalayer, this Python-based server enables downloading NASA Earth Data granules directly into Jupyter notebooks through a simple interface. The implementation leverages jupyter-kernel-client and jupyter-nbmodel-client to programmatically create and execute code cells in notebooks, allowing for parameterized data retrieval with options for temporal ranges and geographic bounding boxes. It's particularly useful for Earth scientists and researchers who need to automate the acquisition of satellite data and integrate it seamlessly into their Jupyter-based analysis workflows.

## Trust

- **Trust score (0–1):** 1.00
- **Verification tier:** verified
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** search
- **Updated:** 2026-09-19

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/datalayer-jupyter-earth)
- **Repository:** <https://github.com/datalayer/jupyter-earth-mcp-server>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "datalayer-jupyter-earth"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/datalayer-jupyter-earth` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/datalayer-jupyter-earth/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt