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MCP server · Developer Tools

Pykernel MCP

by · github.com/DOsinga/pykernel_mcp

MCP server providing persistent IPython kernel for executing Python code with numpy, pandas, and matplotlib

Unclaimed MIT last commit a year ago devtools
InstallView source
47Fair

Scored 6 months ago · breakdown

About Pykernel MCP

Pykernel MCP is an MCP server published by DOsinga in the Developer Tools category: mCP server providing persistent IPython kernel for executing Python code with numpy, pandas, and matplotlib.

The repository has 2 stars and 0 forks, with the last commit a year ago. Six months or more without a commit doesn't mean the server is broken, but check the open issues before depending on it in production.

Install

From source
git clone https://github.com/DOsinga/pykernel_mcp

This server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.

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Security checks

Static checks derived from the repository 7 days ago. Heuristics on the source, not an audit.

  • ✓README presentThe repository ships documentation.
  • ✓License declaredUsable under a stated license.
  • !Tests presentNo tests found. Behaviour changes may go unnoticed between versions.
  • ✓Dependencies pinnedA lockfile pins dependency versions.
  • ✓No dynamic code executionSource scan found no eval, dynamic Function, or shell execution.
  • !Scoped permissionsDoesn't declare a permission scope. Assume it can do anything its process can.

README

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.

Test

Just execute:

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

Installation

Click the button to install:

Install in Goose

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".

README fetched from the repository 7 days ago. The original is authoritative.

Questions

About Pykernel MCP

How do I install Pykernel MCP?

No package has been published for this server, so install it from source: clone the repository and follow its README.

Is Pykernel MCP safe to use with an AI agent?

Its trust score is 47 out of 100 (fair). It passes 4 of the 6 static security checks run so far; the failures are listed above. It has no ConduID identity yet, so agent calls to it are not receipted.

Is Pykernel MCP still maintained?

The last commit was a year ago. That's long enough that you should check whether the maintainer is responding to issues before depending on it.