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jacob-bd

notebooklm-mcp

by jacob-bd
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
Python
Local

notebook_query

Ask questions against sources already uploaded to a NotebookLM notebook to get AI answers grounded in those existing materials.

Instructions

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.

Args: notebook_id: Notebook UUID query: Question to ask source_ids: Source IDs to query (default: all) conversation_id: For follow-up questions timeout: Wall-clock query budget in seconds (default: from env NOTEBOOKLM_QUERY_TIMEOUT or 120.0; source-heavy notebooks may need 180+) new_conversation: Start a fresh conversation when conversation_id is omitted

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
timeoutNo
source_idsNo
notebook_idYes
conversation_idNo
new_conversationNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changedv0.9.12
    • addedInput schema / properties / new_conversation
      Added value: +{
+  "default": false,
+  "type": "boolean"
+}
    • changedInput schema / properties / source_ids / anyOf
      Previous value: -[
-  {
-    "items": {
-      "type": "string"
-    },
-    "type": "array"
-  },
-  {
-    "type": "null"
-  }
-]New value: +[
+  {
+    "type": "string"
+  },
+  {
+    "items": {
+      "type": "string"
+    },
+    "type": "array"
+  },
+  {
+    "type": "null"
+  }
+]
  2. Addedv0.9.1
  3. Removedv0.8.9
  4. Changed5 schema fields changedv0.7.7
    • removedInput schema / properties / conversation_id / description
      Removed value: -"For follow-up questions"
    • removedInput schema / properties / notebook_id / description
      Removed value: -"Notebook UUID"
    • removedInput schema / properties / query / description
      Removed value: -"Question to ask"
    • removedInput schema / properties / source_ids / description
      Removed value: -"Source IDs to query (default: all)"
    • removedInput schema / properties / timeout / description
      Removed value: -"Request timeout in seconds (default: from env NOTEBOOKLM_QUERY_TIMEOUT or 120.0)"
  5. First observedv0.6.13

TDQS

A4.5/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are present, so the description carries the disclosure burden. It discloses the restriction to existing sources, the timeout budget behavior, and conversation handling. It does not mention authentication or side effects, but since the tool appears read-only and the description conveys that, it is adequate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short, front-loaded with the primary purpose and a clear exclusion, then a compact Args list. It wastes no words while covering all parameters.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 6 parameters incl. optional ones, the description provides defaults, environment fallback, and usage notes. Output schema exists (per context) so return values need not be described. Comprehensive enough for an agent to operate without further lookup.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema shows 6 params and 0% description coverage, but the description explains all six parameters: notebook_id, query, source_ids (default all), conversation_id (follow-up), timeout (default from env or 120, 180+ recommendation), and new_conversation. That adds significant meaning beyond the raw types.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description opens with 'Ask AI about EXISTING sources already in notebook' – a specific verb and resource. It immediately excludes the alternative use case ('NOT for finding new sources') and names the sibling tool (research_start) that handles that. This leaves no ambiguity about what the tool does.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use (ask AI about existing notebook sources) and when not to (finding new sources), and points to the alternative tool. Clear contextual cues for selection among siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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