PyKernel MCP

MCP server to make it possible for an agent to execute python in a Jupyter kernel.

Features

PyKernel provides a persistent IPython kernel environment for executing Python code through the Model Context Protocol. After setting this server up, your agent will be able to:

• Maintains state between executions - variables, imports, and functions persist across tool calls

• Pre-loaded scientific stack - comes with numpy, pandas, and matplotlib already imported

• Rich output support - captures text output, errors, and matplotlib plots

• Visualizations - inline matplotlib plots rendered as images

• Package installation - install additional packages on-the-fly with the install_package tool

• Kernel management - restart the kernel to clear state when needed

Use Cases

• Quick data analysis and exploration without writing files

• Iterative computation where you build on previous results

• Mathematical calculations and statistical analysis

• Data visualization with matplotlib

• Testing Python code snippets

• Prototyping algorithms with maintained state

The kernel automatically handles execution timeouts, captures both stdout and stderr, and provides detailed error tracebacks when code fails.

Related MCP server: scicompute-mcp

Test

Just execute:

npx @modelcontextprotocol/inspector uv run src/pykernel_mcp/server.py

Installation

Click the button to install:

Or install manually:

Go to Advanced settings -> Extensions -> Add custom extension. Name to your liking, use type STDIO, and set the command to uvx pykernel-mcp. Click "Add Extension".