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_packagetool - 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.
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".
