notebooklm-mcp

Overview Schema Source Related Servers Discussions Admin

cross_notebook_query

Ask one question across multiple NotebookLM notebooks and get per-notebook answers with sources. Isolates failures so one bad notebook doesn't abort the batch.

Instructions

Ask the same question across multiple notebooks and return per-notebook answers.

Returns { question, results: { <notebook_id>: { answer, source_count, session_ids, chunks } | { error: }, ... }, ok_count, error_count, }

Per-notebook failures (no sources, RPC errors) are captured in error so one bad notebook doesn't abort the fan-out.

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_ids": "<notebook_ids>",
      "question": "<question>"
    },
    "name": "cross_notebook_query"
  }
}

Input Schema

Name	Required	Description	Default
`question`	Yes
`notebook_ids`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Aug 2, 2026

TDQS

A4/5.0

Behavior4/5

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

With no annotations, the description carries the full burden. It discloses key behaviors: per-notebook failure handling (errors captured per notebook, no abort) and the overall return envelope. This goes beyond what the schema provides.

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?

The description is concise and efficiently structured: a one-line purpose, a return type block, and an error-handling note. Every sentence adds value without unnecessary verbosity.

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?

The description covers the core purpose, return structure, and error isolation, which is sufficient for a simple two-parameter tool. It could be more explicit about when to use this instead of notebook_query, but overall it's reasonably complete.

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

Parameters3/5

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

Schema coverage is 0%, so the description must add meaning. It clarifies that 'question' is shared across notebooks and that results are keyed by notebook_ids. However, it doesn't describe the expected format or constraints of the two parameters beyond the schema.

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

Purpose4/5

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

The description states a specific verb (ask) and resource (notebooks) with scope ('across multiple notebooks'). It clearly implies a fan-out query, distinguishing it from the sibling notebook_query tool, though it doesn't explicitly name the alternative.

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

Usage Guidelines4/5

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

The phrase 'Ask the same question across multiple notebooks' conveys the primary use case: querying multiple notebooks in one call. It doesn't state when-not-to-use or contrast with single-notebook querying, but the context is clear.

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