Skip to main content
Glama
dillettante

NotebookLM Retrieval MCP

by dillettante
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Related Servers

Alternatives to NotebookLM Retrieval MCP

No user-submitted related servers found.

    Related Servers

    • NotebookLM MCP Server

      KimMarinet
      F
      license
      A
      quality
      D
      maintenance
      Enables AI clients to manage NotebookLM notebooks, including listing, creating, and adding web sources, using Chrome authentication.
      3
      -

    • mcp-notebooklm

      juanQNav
      A
      license
      A
      quality
      C
      maintenance
      Exposes Google NotebookLM notebooks as tools for AI assistants, enabling listing, finding, and querying notebooks with grounded answers.
      4
      1
      MIT

    • notebooklm-mcp

      jacob-bd
      A
      license
      B
      quality
      A
      maintenance
      Enables AI assistants to programmatically interact with Google NotebookLM, allowing them to create and manage notebooks, add sources, query content, generate audio/video, and perform research tasks through natural language commands.
      53
      13,623 PyPI
      6,246
      MIT

    • notebooklm-mcp-cli

      hungln105-cell
      A
      license
      B
      quality
      B
      maintenance
      Enables AI assistants to programmatically access and manage Gemini Notebook (formerly Google NotebookLM), including creating notebooks, adding sources, querying content, generating studio content, downloading artifacts, and running batch or cross-notebook workflows through natural language.
      49
      MIT
    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A3.7/5.0

    Scored across 5 tools

    Disambiguation5/5

    Each tool targets a distinct action-resource pair: list_notebooks vs list_sources operate on different entities, source_fulltext vs notebook_summary clearly separate raw source text from derived AI summaries, and ask is a uniquely generative operation. Descriptions explicitly clarify the raw-vs-derived distinction, leaving no real overlap.

    Naming Consistency3/5

    Names mix conventions: list_notebooks and list_sources follow verb_noun, but source_fulltext and notebook_summary are noun_noun, and ask is a bare verb. Still readable and inferable, but not a predictable pattern.

    Tool Count5/5

    Five focused tools are well-scoped for a read-only retrieval server, each earning its place without redundancy or filler.

    Completeness4/5

    The read-only retrieval surface covers notebook listing, source listing, full text, summaries, and Q&A, matching the server's stated retrieval purpose. Minor gaps exist (no cross-notebook source search or content-write operations), but agents can work around them within the read-only scope.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources