"A tool for executing code in Jupyter Notebooks" matching MCP servers:

Enables AI agents to interact with Jupyter notebooks via MCP tools for querying, modifying, executing, and setting up notebooks, with state preservation and real-time collaboration.

Enables programmatic interaction with Jupyter notebooks, allowing reading, editing, and executing cells via Claude.

A self-hosted MCP server providing Claude with an interactive Jupyter environment, including real kernels, persistent notebooks, and optional GPU offload to Google Colab.

A configurable MCP server extension for Jupyter Server that enables dynamic registration of Python functions as tools accessible to MCP clients from a running Jupyter Server.

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

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

An MCP server for programmatically editing Jupyter notebooks, offering 29 tools for reading, modifying, and batch-processing notebooks without requiring a Jupyter server.

Enables interaction with Jupyter notebooks through the Model Context Protocol, supporting code execution and markdown insertion within JupyterLab environments.

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.

A FastMCP server for loading, editing, searching, and saving Jupyter notebooks (.ipynb) through MCP tools. It maintains a single active notebook session with live cell indices that update as cells are inserted or removed.

An MCP server that enables AI to connect and manage Jupyter Notebooks in real-time, with support for cell operations, execution, and multimodal outputs.

Enables agents to synthesize typed MCP tool schemas at runtime, validate Python ASTs for unsafe imports, compile sandboxed callable handlers, and hot-plug them into an active MCP runtime. It provides a zero-dependency, standard-library-only MCP server for safe just-in-time tool creation and execution.

Enables AI agents to interact with collaborative Jupyter notebooks and documents in real-time, supporting notebook operations, document editing, user awareness tracking, and session management through Jupyter's RTC capabilities.

A Model Context Protocol server implementation that provides geospatial analysis tools in Jupyter notebooks, particularly for downloading NASA Earth data granules.

Automates Jupyter Book notebook creation by fixing common rendering issues (bold, math, markdown formatting) and provides tools for adding cells, validating notebooks, updating TOC, and deploying.

Call OpenAI Codex from Claude Code for independent second opinions, structured code review, and delegated coding tasks through a FastMCP plugin that drives the codex CLI safely.

MCP router that fronts your other MCP servers behind a few small generic tools, so their tool definitions stay out of the context window and are discovered on demand.

Enables MCP clients like Claude to query Chinese A-share market data, including K-line charts with forward/backward adjustment, limit-up and limit-down pools, per-stock fund flows, index quotes, and stock news, with automatic symbol normalization and retry handling for throttled data sources.

Provides A-share (Chinese stock market) quantitative analysis through tools for stock screening, northbound capital flow tracking, dragon-tiger list analysis, margin trading, sector analysis, technical indicators, IPO info, and limit-up/down statistics using akshare data.

Enables users to describe their LLM fine-tuning job once and get the cheapest, fastest, and most balanced GPU options across a dozen cloud providers in seconds.