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Merge pull request #2467 from roomi-fields/add-notebooklm-mcp

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Add NotebookLM MCP Server
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Frank Fiegel
2026-03-06 13:20:32 -05:00
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@@ -1161,6 +1161,7 @@ Persistent memory storage using knowledge graph structures. Enables AI models to
- [pomazanbohdan/memory-mcp-1file](https://github.com/pomazanbohdan/memory-mcp-1file) 🦀 🏠 🍎 🪟 🐧 - A self-contained Memory server with single-binary architecture (embedded DB & models, no dependencies). Provides persistent semantic and graph-based memory for AI agents.
- [ragieai/mcp-server](https://github.com/ragieai/ragie-mcp-server) 📇 ☁️ - Retrieve context from your [Ragie](https://www.ragie.ai) (RAG) knowledge base connected to integrations like Google Drive, Notion, JIRA and more.
- [redleaves/context-keeper](https://github.com/redleaves/context-keeper) 🏎️ 🏠 ☁️ 🍎 🪟 🐧 - LLM-driven context and memory management with wide-recall + precise-reranking RAG architecture. Features multi-dimensional retrieval (vector/timeline/knowledge graph), short/long-term memory, and complete MCP support (HTTP/WebSocket/SSE).
- [roomi-fields/notebooklm-mcp](https://github.com/roomi-fields/notebooklm-mcp) ([glama](https://glama.ai/mcp/servers/@roomi-fields/notebooklm-mcp)) 📇 🏠 🍎 🪟 🐧 - Full automation of Google NotebookLM — Q&A with citations, audio podcasts, video, content generation, source management, and notebook library. MCP + HTTP REST API.
- [rushikeshmore/CodeCortex](https://github.com/rushikeshmore/CodeCortex) [glama](https://glama.ai/mcp/servers/@rushikeshmore/codecortex) 📇 🏠 🍎 🪟 🐧 - Persistent codebase knowledge layer for AI coding agents. Pre-digests codebases into structured knowledge (symbols, dependency graphs, co-change patterns, architectural decisions) via tree-sitter native parsing (28 languages) and serves via MCP. 14 tools, ~85% token reduction. Works with Claude Code, Cursor, Codex, and any MCP client.
- [shinpr/mcp-local-rag](https://github.com/shinpr/mcp-local-rag) 📇 🏠 - Privacy-first document search server running entirely locally. Supports semantic search over PDFs, DOCX, TXT, and Markdown files with LanceDB vector storage and local embeddings - no API keys or cloud services required.
- [Smart-AI-Memory/empathy-framework](https://github.com/Smart-AI-Memory/empathy-framework) 🐍 🏠 - Five-level AI collaboration system with persistent memory and anticipatory capabilities. MCP-native integration for Claude and other LLMs with local-first architecture via MemDocs.