notebooklm-mcp

Overview Schema Source Related Servers Discussions Admin

notebook_query

Ask a question in a NotebookLM notebook and receive a grounded answer with source citations. The query persists in the notebook's chat history for future reference.

Instructions

Ask a question inside a notebook and return the grounded answer.

The exchange is persisted in the NotebookLM web UI's chat history. Returns { answer, source_count, session_ids, chunks }.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "notebook_id": "<notebook_id>",
      "question": "<question>"
    },
    "name": "notebook_query"
  }
}

Input Schema

Name	Required	Description	Default
`question`	Yes
`notebook_id`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Aug 2, 2026

TDQS

A3.7/5.0

Behavior3/5

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

With no annotations, the description must disclose behavioral traits on its own. It does reveal a side effect (the exchange is persisted in NotebookLM's chat history) and previews the return shape, which is useful. However, it does not address whether the operation is read-only, if it requires special authentication, or if there are rate limits or costs, leaving a partial transparency gap.

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

Conciseness5/5

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

Two short sentences, front-loaded with the primary purpose, followed by the key side effect and return fields. No fluff, every sentence earns its place.

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?

For a two-parameter tool with an output schema, the description covers the core operation, side effect, and return shape. It is concise and complete enough for straightforward use, though it lacks explicit guidance on when to use it or how it differs from related tools.

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

Parameters2/5

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

Schema description coverage is 0%, so the description should compensate by explaining the parameters. The phrase 'inside a notebook' hints at notebook_id and 'Ask a question' hints at question, but it does not clarify formats, constraints, or relationships. It adds minimal meaning beyond the parameter names.

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?

The description clearly states the verb ('Ask a question') and resource ('inside a notebook'), and specifies the outcome ('return the grounded answer'). It distinguishes itself from siblings like cross_notebook_query and research_and_ask by focusing on a single notebook and a chat-persisted exchange.

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

Usage Guidelines3/5

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

The description implies usage: you use this tool when you need to ask a question within a specific notebook. However, it does not explicitly say when to prefer this over alternatives like cross_notebook_query or research_and_ask, nor does it mention any exclusions or prerequisites.

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