notebooklm-mcp-cli

# NotebookLM MCP CLI ## Source - Upstream repository: `https://github.com/jacob-bd/notebooklm-mcp-cli` - Local checkout: `%USERPROFILE%\.notebooklm-mcp-cli\notebooklm-mcp-cli` - Installed package: `notebooklm-mcp-cli` - CLI command: `nlm` - MCP server command: `notebooklm-mcp` - Universal stack source registry: `%USERPROFILE%\.universal-ai-stack\config\source-integrations.json` ## Operating Model Use the CLI first. The MCP server is available for clients that require a live tool endpoint, but it should not be started or registered by default for simple one-shot NotebookLM work. Do not copy this upstream repository, cookies, browser profiles, generated media, or NotebookLM session state into AI roots or into this repo. The universal install uses: - the PyPI/uv tool install for executables, - one external source checkout for README-grounded reference, - this one canonical skill for routing and workflow instructions, - compact `skill-router` wrappers in each AI client. ## Authentication NotebookLM access depends on the user's Google account session. Check auth before doing NotebookLM operations: ```powershell nlm login --check ``` If auth is missing, use the upstream login flow: ```powershell nlm login ``` For multiple Google accounts: ```powershell nlm login --profile work nlm login --profile personal nlm login profile list nlm login switch work ``` Never print, paste, store, or commit browser cookies, Google OAuth state, downloaded private source files, or NotebookLM-generated private artifacts. ## CLI Workflows Use `nlm --ai` for the upstream's full AI-assistant command reference when exact syntax is needed. Common commands: ```powershell nlm notebook list nlm notebook create "Research Project" nlm source add <notebook> --url "https://example.com/article" nlm source add <notebook> --file "C:\path\source.pdf" nlm notebook query <notebook> "What are the key findings?" nlm cross query "Compare these notebooks on the main risks" nlm studio create <notebook> audio --confirm nlm download audio <notebook> <artifact-id> nlm share public <notebook> nlm doctor ``` Capabilities to prefer this skill for: - notebook list/create/delete workflows, - adding URL, text, Drive, and file sources, - source-grounded notebook queries, - cross-notebook research, - batch query/create/delete operations, - pipeline run/list workflows, - tags and smart notebook selection, - audio overview, video, slide, and other Studio artifacts, - artifact download, - public or invite sharing, - Google Drive source sync, - NotebookLM MCP configuration and troubleshooting. ## MCP Configuration Only configure MCP when the target AI client explicitly needs an MCP server. For universal stack work, prefer direct CLI calls or `skill-router` routing. Generate a generic MCP config when needed: ```powershell nlm setup add json ``` Use upstream client-specific setup only after checking existing client config to avoid duplicate MCP entries: ```powershell nlm setup list nlm setup add claude-code nlm setup add gemini nlm setup add cursor ``` If a client already has the compact universal skill-router adapter, do not install a full duplicate NotebookLM skill tree into that client. Keep the MCP server as an optional stdio command: ```text notebooklm-mcp ``` ## Quality Gates Before reporting this integration healthy, verify: ```powershell nlm --version notebooklm-mcp --help nlm doctor skill-router skill notebooklm-mcp-cli skill-router preflight --json "query my NotebookLM notebook about this research" skill-router skills validate-manifest ``` `nlm doctor` may report that Google login is missing. That is an auth state, not a failed installation, unless the user expected the account to be logged in already.