nteract/claude-plugin-nightly/repl

Use a Notebook Instead of python3 -c

When you have nteract MCP tools available and you're about to do multi-step Python work — chaining python3 -c commands, writing a throwaway .py script, or running exploratory code — use a notebook instead. You get persistent state between cells, rich output (tables, plots, errors with tracebacks), and a shareable .ipynb file.

Quick Start

create_notebook(path="~/analysis.ipynb")
create_cell(source="import pandas as pd\ndf = pd.read_csv('data.csv')\ndf.head()", cell_type="code", and_run=true)

Core Workflow

1. Start a notebook:

create_notebook(path="~/analysis.ipynb") — creates and opens it.

1. Add and run code cells:

create_cell(source="your code here", cell_type="code", and_run=true) — creates the cell AND executes it in one call. State persists: variables from earlier cells are available in later ones.

1. Iterate on a cell:

set_cell(cell_id="...", source="updated code") then execute_cell(cell_id="...") — edit and re-run without creating a new cell.

1. Check your work:

get_all_cells(format="summary", include_outputs=true) — see all cells with output previews at a glance.

1. Save when done:

save_notebook() — writes the .ipynb to disk.

When to Use This

• Exploring a dataset (load, filter, plot, iterate)
• Running multi-step computations where later steps depend on earlier results
• Generating visualizations (matplotlib, plotly, altair)
• Prototyping code that you'll refine over several iterations
• Any task where you'd otherwise chain 3+ python3 -c commands

When NOT to Use This

• One-shot commands (python3 -c "print(2+2)" is fine as-is)
• Running existing scripts (python3 script.py)
• Non-Python tasks