A JupyterLab extension to manage MCP servers.
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# jupyter_mcp_manager [](https://github.com/jupyter-ai-contrib/jupyter-mcp-manager/actions/workflows/build.yml) **A JupyterLab extension for managing Model Context Protocol (MCP) servers.** This extension provides a user interface and backend infrastructure to configure and manage MCP servers directly within JupyterLab. It allows you to define which MCP servers are available for use by other JupyterLab extensions or AI assistants.  ## What is MCP? The **Model Context Protocol (MCP)** is an open standard that enables AI assistants and other clients to interact with various data sources, tools, and services through a unified interface. MCP servers expose resources, tools, and prompts that AI models can query and use during conversations. This extension manages the MCP server configurations that other JupyterLab extensions (like `jupyter_server_mcp`) or external clients can use to connect to and interact with MCP servers. ## Features - **Configuration Management**: Add, edit, remove and disable MCP server configurations through a dedicated settings panel in JupyterLab - **Multiple Server Types**: Support for both **HTTP** (remote endpoint) and **stdio** (local executable) MCP servers - **Flexible Configuration**: Configure servers via: - JupyterLab Settings UI (persisted in browser) - JSON configuration files in Jupyter config directories (`~/.jupyter/mcp_servers.json`) - adding MCP servers in the manager server side (for extensions) - **Environment Variables & Headers**: Set HTTP headers for HTTP servers and custom environment variables for stdio servers - **Real-time Updates**: Automatic synchronization between frontend settings and backend configuration ## Requirements - JupyterLab >= 4.0.0 - Python >= 3.10 ## Install To install the extension, execute: ```bash pip install jupyter_mc…
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