Model Context Protocol (MCP) server designed to allow AI agents within Cursor to interact with Jupyter Notebook (.ipynb) files

Lightweight Python Notebook MCP - Enable AI assistants to create, edit, and view Jupyter notebooks via Model Context Protocol

Turn topics, links, and files into AI-generated research notebooks — summarize, explore, and ask anything.

🪐 ✨ AI Agents for JupyterLab with 🛠️ MCP tools - Chat interface for intelligent notebook interaction, code execution, and workspace management.

Nanoservices for notebooks.

chm to markdown and vectorDB

A Model Context Protocol (MCP) server that provides AI assistants with access to Microsoft OneNote. This server enables AI models to read from and write to OneNote notebooks, sections, and pages.

Model Context Protocol (MCP) server for Databricks that empowers AI agents to autonomously interact with Unity Catalog metadata. Enables data discovery, lineage analysis, and intelligent SQL execution. Agents explore catalogs/schemas/tables, understand relationships, discover notebooks/jobs, and execute queries - greatly reducing ad-hoc query time.

This repository is a collection of Jupyter notebooks I've managed to collect from exercises from various courses and online sources.

🤖 Connect your CLI agents with NotebookLM for accurate answers based on your notebooks, reducing errors and enhancing productivity.

This repo contains example notebooks and MCP samples created while learning the Model Customization Protocol (MCP) through a DeepLearning.AI course in collaboration with Anthropic, the creators of MCP, and the deep learning community who contributed to the course.

MCP server to let claude connect to my obsidian notes for vector and full text search

🧪 Explore and test MCP servers with fastmcp and FastAgent. List tools, invoke APIs, and augment LLMs with real-time capabilities using OpenAI, Claude, and Brave Search.

Jupyter notebooks from the DeepLearning.AI course “MCP: Build Rich-Context AI Apps with Anthropic.”

[MCP Tool] Jupyter, let the LLM perform Jupyter Notebook "reproducible" analysis — Derived from the official Jupyter MCP Server by @datalayer.tech

AI Learning: A comprehensive repository for Artificial Intelligence and Machine Learning resources, primarily using Jupyter Notebooks and Python. Explore tutorials, projects, and guides covering foundational to advanced concepts in AI, ML, DL and Gen/Agentic Ai.

Real-world examples and testing tools for the Microsoft Fabric Model Context Protocol (MCP) server. Learn to automate Fabric operations including lakehouse management, notebook execution, and data population workflows.

This repository provides the companion Jupyter Notebooks for the book Context-Engineering for Multi-Agent Systems. It technically details the progressive construction of a Context Engine moving from simple prompts to foundational concepts to a production-ready system semantic blueprint. The code is a guide to building Multi-Agent Systems (MAS)