Graphify-Labs/graphify
Graphify transforms codebases, documentation, and other files into queryable knowledge graphs, enhancing developer productivity with local AST parsing and detailed edge explanations.
Awesome MCP › Software Development
JupyterMCP gives an MCP client control of a live Jupyter Notebook session. It has three components: a WebSocket server started inside Jupyter that bridges the notebook to external clients; a JavaScript client loaded in the notebook page that performs the actual operations, such as inserting a cell or running code; and a Python MCP server that implements the protocol and relays requests to the WebSocket bridge. Through this chain a client can insert new cells, execute a specific cell or every cell in the notebook, retrieve the output of an execution with limits on how much text is returned, query information about the notebook, and save it. The two-way channel means results of a computation come back to the model, so iterative work — write code, run it, read the error, revise — happens inside the notebook the user is already looking at rather than in a separate sandbox. The project states a narrow compatibility boundary: it works only with Jupyter Notebook version 6.x, and explicitly not with JupyterLab, Notebook 7.x, VS Code notebooks or Google Colab, because it depends on the classic notebook's JavaScript extension model. It requires Python 3.12 or newer. The audience is analysts and researchers doing exploratory data work in classic Jupyter who want an assistant operating the same notebook.
https://github.com/jjsantos01/jupyter-notebook-mcp
Graphify transforms codebases, documentation, and other files into queryable knowledge graphs, enhancing developer productivity with local AST parsing and detailed edge explanations.
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