Jupyter MCP Server is a Model Context Protocol (MCP) server implementation that provides interaction with Jupyter notebooks running in a local JupyterLab. It allows users to execute code and markdown cells in real-time within the Jupyter environment, even when integrated with tools like Claude Desktop.
Jupyter MCP Server enables seamless interaction between external applications (like Claude Desktop) and Jupyter notebooks through its MCP implementation. This is particularly useful for collaborative workflows, automation, or integrating Jupyter into other platforms while leveraging features like Jupyter Real Time Collaboration (RTC).
Developers, data scientists, researchers, and teams working on collaborative projects that require integration of Jupyter notebooks with external tools or platforms would benefit from Jupyter MCP Server. It is also suitable for users who want to automate notebook interactions via APIs.
Jupyter MCP Server can be used locally on your machine, within Docker containers, or as part of cloud-based environments where Jupyter notebooks are employed. It is compatible with MacOS, Windows, and Linux operating systems.
You should use Jupyter MCP Server when you need to interact programmatically with Jupyter notebooks, especially in scenarios involving real-time collaboration, automated workflows, or integration with third-party tools like Claude Desktop.
To start JupyterLab, ensure you have installed the required packages (`jupyterlab`, `jupyter-collaboration`, and `ipykernel`) using pip. Then run: `jupyter lab --port 8888 --IdentityProvider.token MY_TOKEN --ip 0.0.0.0`. Replace `MY_TOKEN` with your desired token.
The server currently offers two main tools: `add_execute_code_cell` to add and execute a code cell in a Jupyter notebook, and `add_markdown_cell` to add a markdown cell to a notebook.
Update your `claude_desktop_config.json` file by specifying the `SERVER_URL`, `TOKEN`, and `NOTEBOOK_PATH`. Ensure these values match those used in the `jupyter lab` command. For example, set `SERVER_URL` to `http://host.docker.internal:8888` on MacOS/Windows or `http://localhost:8888` on Linux.
Yes, you can build it using Docker with the following command: `docker build -t datalayer/jupyter-mcp-server .`.
Yes, you can install it automatically via Smithery using this command: `npx -y @smithery/cli install @datalayer/jupyter-mcp-server --client claude`.
MCP (Model Context Protocol) is an open protocol designed to standardize how applications provide context information to large language models (LLMs). Like a 'USB-C port' for AI applications, MCP ensures AI models can seamlessly connect with various data sources and tools.
An MCP Server is a server that supports the MCP protocol, enabling the exchange of contextual information between applications and AI models in a standardized way. It provides developers with an easy way to integrate AI models with databases, APIs, or other data sources.
An MCP Server eliminates the complexity of developing custom adapters by unifying the connection between AI models and various data sources. Whether you're a developer, data scientist, or AI app builder, an MCP Server simplifies the integration process, saving time and resources.
An MCP Server acts as an intermediary bridge, converting contextual information from various data sources into a format that AI models can understand. By adhering to the MCP protocol, it ensures data is transmitted between applications and AI models in a standardized manner.
At mcpserver.shop, you can browse our MCP Server Directory. The directory is categorized by industry (e.g., finance, healthcare, education), and each server comes with detailed descriptions and tags to help you quickly find the option that suits your needs.
The MCP Server Directory on mcpserver.shop is free to browse. However, some servers are hosted by third-party providers and may involve usage fees. Check the detailed page of each server for specific information.
MCP Servers support a wide range of data sources, including databases, APIs, cloud services, and custom tools. The flexibility of the MCP protocol allows it to connect almost any type of data source to AI models.
MCP Servers are primarily designed for developers, data scientists, and AI app builders. However, mcpserver.shop provides detailed documentation and guides to help users of varying technical levels get started easily.
Yes, MCP is an open-source protocol that encourages community participation and collaboration. For more details or to contribute, visit the official MCP documentation.
On mcpserver.shop, each MCP Server’s detailed page includes the provider’s contact information or a link. You can directly reach out to the provider for more details or technical support.