by capitalparser · MCP Server · ★ 64
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NotebookLM Wiki Pipeline 한국어 README Turn large Google Drive PDFs into Obsidian wiki notes without loading the full PDF text into Claude or Codex context. NotebookLM reads the source, and the agent receives only the structured answer needed to create a reusable note. vNext update: reuse one NotebookLM notebook per topic, while each new note-generation query is scoped to the newly attached or selected PDF source. That is the main product value: The screenshot above is an actual NotebookLM notebook screen from a live MCP test using original public-safe demo PDFs. The topic notebook contains three related infrastructure sources: clean energy grid planning, urban water resilience, and public transit operations. For note generation, the MCP call used , and NotebookLM returned with only the target clean-energy source. Why This Matters The old safe pattern was: That avoids source contamination, but it does not scale well. Users who process many PDFs about the same topic end up with scattered one-off notebooks. Th
| Stars | 64 |
| Forks | 6 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 69.499472736802/100 |
| Open Issues | 3 |
| Last Updated | 2026-05-25 |
| Created | 2026-05-10 |
| Platforms | claude-code, mcp, python |
| Est. Tokens | ~48k |
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notebooklm-wiki-pipeline is Turn Google Drive PDFs into Obsidian wiki notes via NotebookLM MCP without loading full PDFs into Claude context. It is categorized as a MCP Server with 64 GitHub stars.
notebooklm-wiki-pipeline is primarily written in Python. It covers topics such as claude-code, google-drive, knowledge-management.
You can find installation instructions and usage details in the notebooklm-wiki-pipeline GitHub repository at github.com/capitalparser/notebooklm-wiki-pipeline. The project has 64 stars and 6 forks, indicating an active community.
notebooklm-wiki-pipeline is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to notebooklm-wiki-pipeline on Agent Skills Hub include ai-skills, mcp-server-code-execution-mode, skill-conductor. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.