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Glama

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": true
}
logging
{}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}

Tools

Functions exposed to the LLM to take actions

NameDescription
refresh_authA

Reload auth tokens from disk or run headless re-authentication.

Call this after running nlm login to pick up new tokens, or to attempt automatic re-authentication if Chrome profile has saved login.

Returns status indicating if tokens were refreshed successfully.

save_auth_tokensA

Save NotebookLM cookies (FALLBACK method - try nlm login first!).

IMPORTANT FOR AI ASSISTANTS:

  • First, run nlm login via Bash/terminal (automated, preferred)

  • Only use this tool if the automated CLI fails

batchA

Perform batch operations across multiple notebooks.

Actions:

  • query: Query multiple notebooks with the same question

  • add_source: Add the same source URL to multiple notebooks

  • create: Create multiple notebooks at once

  • delete: Delete multiple notebooks (IRREVERSIBLE, requires confirm=True)

  • studio: Generate studio artifacts across multiple notebooks

notebook_queryA

Ask AI about EXISTING sources already in notebook. NOT for finding new sources.

Use research_start instead for: deep research, web search, find new sources, Drive search.

chat_configureC

Configure notebook chat settings.

notebook_query_startA

Start a notebook query asynchronously for source-heavy notebooks or long questions.

Use this instead of notebook_query when the response may take longer than the default 120-second budget. A timeout around 180 seconds is a useful starting point for source-heavy notebooks. Returns immediately with a query_id. Poll notebook_query_status with the query_id to get the result.

Workflow: notebook_query_start -> poll notebook_query_status until completed.

notebook_query_statusA

Check the status of an async notebook query started with notebook_query_start.

Returns the query result when completed, or current status if still in progress. Poll this tool every few seconds until status is 'completed' or 'error'.

chat_listA

List chat sessions for a notebook.

chat_getB

Get full transcript of a specific chat session.

chat_exportC

Export a chat transcript to Markdown or JSON.

collection_listA

List all native collections.

collection_createC

Create a new collection.

collection_editA

Edit an existing collection's name and/or list of notebooks.

collection_set_emojiA

Set or clear the emoji marker on a collection.

collection_deleteA

Delete a collection permanently. Notebooks inside the collection are NOT deleted.

cross_notebook_queryA

Query multiple notebooks and get aggregated answers with per-notebook citations.

Specify notebooks by name, by tags, or use all=True for all notebooks.

download_artifactB

Download any NotebookLM artifact to a file.

Unified download tool replacing 9 separate download tools. Supports all artifact types: audio, video, report, mind_map, slide_deck, infographic, data_table, file, quiz, flashcards.

download_all_artifactsA

Download all completed studio artifacts of one notebook — or every notebook.

Creates a subdirectory of output_dir named after each notebook title and saves every completed artifact there, named after its title with the type's default extension (report → .md, mind_map → .json, video → .mp4, slide_deck → .pdf/.pptx, ...). Artifacts that are still generating or failed are skipped and listed in the result. A failure on one artifact (or one notebook in a sweep) does not stop the others.

export_artifactA

Export a NotebookLM artifact to Google Docs or Sheets.

Supports:

  • Data Tables → Google Sheets

  • Reports (Briefing Doc, Study Guide, Blog Post) → Google Docs

labelA

Manage source labels in a notebook. Unified tool for all label operations.

Labels let you organize sources into thematic categories. Requires 5+ sources for auto-labeling. Sources can belong to multiple labels simultaneously.

Supports: auto, list, reorganize, create, rename, set_emoji, move_source, delete

notebook_listC

List all notebooks.

notebook_getC

Get notebook details with sources.

notebook_describeA

Get AI-generated notebook summary with suggested topics.

notebook_createB

Create a new notebook.

notebook_renameC

Rename a notebook.

notebook_deleteA

Delete notebook permanently. IRREVERSIBLE. Requires confirm=True.

noteB

Manage notes in a notebook. Unified tool for all note operations.

Supports: create, list, update, delete

pipelineB

Manage and execute multi-step notebook pipelines.

Actions:

  • run: Execute a pipeline on a notebook

  • list: List all available pipelines (builtin and user-defined)

research_startA

Deep research / fast research: Search web or Google Drive to FIND NEW sources.

Use this for: "deep research on X", "find sources about Y", "search web for Z", "search Drive". Workflow: research_start -> poll research_status -> research_import.

research_statusA

Poll research progress. Blocks until complete or timeout.

research_importA

Import discovered sources into notebook.

Call after research_status shows status="completed".

server_infoA

Get version, auth status, and conservative MCP capability visibility.

AI assistants: If update_available is True, inform the user that a new version is available and suggest updating with the provided command.

auth_status is the result of an AuthHealthChecker probe. The checker runs a multi-probe strategy (homepage fetch + API fallback) with 30-second TTL caching and mtime-based bypass on auth-file changes. The reported value may therefore be up to 30 seconds old, and an external nlm login is picked up within one check cycle without waiting for the TTL to expire.

auth_status meanings:

  • "configured" — homepage (or API fallback) check passed; credentials are good. Cached credentials may be reported as configured for up to 30 seconds.

  • "not_configured" — no credentials are stored (first-time setup).

  • "stale" — credentials are known-bad (expired or past the 7-day heuristic). Operations will fail; ask the user to run nlm login to refresh.

  • "unverified" — the check could not be completed (network error, timeout, non-200 response). Cached credentials may still work for actual API calls, so do not assume the user needs to re-auth.

  • "error" — unexpected exception inside the check itself.

Returns: dict with version info: - version: Current installed version - latest_version: Latest version on PyPI (or None if check failed) - update_available: True if a newer version is available - auth_status: configured | stale | unverified | not_configured | error - update_command: Command to run to update - mcp_capabilities: Built-in tool groups visible in this server process - provider_capabilities: Explicitly unprobed provider/account capabilities

notebook_share_statusA

Get current sharing settings and collaborators.

notebook_share_publicA

Enable or disable public link access.

notebook_share_inviteC

Invite a collaborator by email.

notebook_share_batchC

Invite multiple collaborators in a single request.

tagA

Manage notebook tags and find relevant notebooks by tag matching.

Actions:

  • add: Add tags to a notebook for smart selection

  • remove: Remove tags from a notebook

  • list: List all tagged notebooks with their tags

  • select: Find notebooks relevant to a query using tag matching

source_addA

Add a source to a notebook. Unified tool for all source types.

Supports: url, text, drive, file

source_list_driveA

List sources with types and Drive freshness status.

Use before source_sync_drive to identify stale sources.

source_sync_driveA

Sync Drive sources with latest content. Requires confirm=True.

Call source_list_drive first to identify stale sources.

source_renameB

Rename a source in a notebook.

source_deleteA

Delete source(s) permanently. IRREVERSIBLE. Requires confirm=True.

source_describeB

Get AI-generated source summary with keyword chips.

source_get_contentA

Get raw text content of a source (no AI processing).

Returns the original indexed text from PDFs, web pages, pasted text, or YouTube transcripts. Much faster than notebook_query for content export.

studio_createC

Create any NotebookLM studio artifact. Unified creation tool.

Supports: audio, video, infographic, slide_deck, report, flashcards, quiz, data_table, mind_map

studio_statusC

Check studio content generation status and get URLs, or rename an artifact.

reportC

Work with an interactive report's embedded elements. Unified report tool.

Create the report itself with studio_create(artifact_type="report", report_format="Interactive"). Then:

studio_deleteA

Delete studio artifact. IRREVERSIBLE. Requires confirm=True.

studio_reviseA

Revise individual slides in an existing slide deck. Creates a NEW artifact.

Only slide decks support revision. The original artifact is not modified. Poll studio_status after calling to check when the new deck is ready.

usage_getA

Show how much of the plan's usage allowance is left, and when it resets.

Gemini Notebook meters usage as compute against two windows at once: a short rolling window and a weekly one. Both are reported, each with the percentage used, the percentage remaining and the reset time in UTC.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

TDQS

B3.1/5.0

Scored across 50 tools

Disambiguation3/5

Most tools separate cleanly by resource (notebook_, source_, collection_, studio_, research_), but several overlap: cross_notebook_query vs. batch's query action, report vs. studio_create, and download_artifact/export_artifact/download_all_artifacts could be confused. Descriptions mostly clarify intent, so an agent can usually pick correctly, but the boundaries are not always crisp.

Naming Consistency2/5

The resource-prefix pattern (notebook_get, collection_list, source_add) is visible but broken by bare-noun umbrella tools like tag, label, note, batch, pipeline, and report. Mixing noun_verb names with action-first names like download_artifact and research_start makes the naming convention unpredictable.

Tool Count2/5

50 tools is far beyond the well-scoped range, even though several tools consolidate many sub-operations. The broad NotebookLM domain justifies some sprawl, but the overall surface is heavy and burdensome for an agent to navigate.

Completeness4/5

The tool set covers notebook/source/collection CRUD, sharing, research workflows, chat, studio artifact creation/download/export/delete, auth, usage, labels, notes, tags, and batch operations. Minor gaps such as collaborator removal or a general artifact listing don't create fatal dead ends.

Maintenance

ActivityActive
ResponsivenessResponsive