"Information about the Linux Kernel" matching MCP servers:

Provides a persistent Jupyter kernel for executing code, inspecting variables and dataframes, and checking SQL query plans, enabling agents to work with stateful Python sessions.

Connects to an existing IPython kernel for persistent Python code execution, preserving variables and state across calls. It enables AI agents and clients to share a live kernel for interactive coding, execution, kernel status checks, and interrupts.

Enables AI agents to execute Python, TypeScript, and JavaScript code in persistent Jupyter kernels with stateful variables and imports across interactions.

Enables AI assistants to perform controlled Linux system administration tasks like reading logs, managing services, cron jobs, WordPress, and executing sandboxed Python code, with strict security constraints.

An MCP server that connects directly to a Jupyter kernel via ZMQ, enabling AI assistants to read, create, edit, execute, and manage Jupyter Notebooks as MCP tools.

Exposes a Linux host with shell, Python, and filesystem tools as MCP tools over HTTPS, enabling AI agents to execute commands and manage files remotely.

Enables AI agents to directly manipulate Jupyter Notebook cells and structure through a secure, well-defined API, overcoming Cursor's AI agent mode limitations.

An MCP server that connects to existing IPython kernels, allowing Claude to execute code in a shared persistent environment with your IDE.

Enables AI agents to execute Jupyter notebook cells with persistent kernel state, output persistence, and structured JSON control surface.

Connects Claude Code to local Jupyter kernels for code execution, kernel management, and notebook execution, with security gating and output capture.

An MCP server that lets AI models run code in 31 languages, evaluate symbolic math and logic problems, and measure complexity—exposed as 48 tools for execution, session management, translation, optimization, and more.

Allows LLMs to execute Wolfram Language code in a secure, session-based environment by providing an interface to interact with a Wolfram Mathematica kernel.

MCP server for Python build123d to help AIs develop and reason about 3D models and CAD

Provides agents with real Linux VMs to run commands, manage files, expose ports, and track costs, all through an ephemeral sandbox service.

An MCP server that exposes the Prova reasoning verifier, enabling AI agents to verify their own reasoning and kernel-check Lean 4 proofs before outputting answers.

An MCP server that manages an IPython kernel, allowing LLMs to execute Python code in a project's virtual environment.

Enables AI assistants to read, edit, and execute Jupyter notebook files (.ipynb) with live kernel output, without requiring JupyterLab.

Data-visualization MCP server for ROOT Workspace. It enables creating and iterating on charts (Vega-Lite, Python plots) with a persistent kernel and vision loop.

MCP server to give every agent an ephemeral Linux sandboxes for executing shell commands.

Enables interactive Python execution with a persistent IPython kernel through MCP, retaining namespace state and providing structured output logs for agent and tool integrations.