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dillettante

NotebookLM Retrieval MCP

by dillettante

ask

Sends a question about a NotebookLM notebook's sources to Google, appends the answer to chat history, and returns citation numbers with source IDs.

Instructions

Ask Google NotebookLM about its sources. Sends the question to Google and appends to the notebook's chat history. Consumes NotebookLM quota. References carry actual citation numbers and source IDs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
notebookYes
questionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=false, idempotentHint=false, and openWorldHint=true, so the write/external profile is partly covered. The description adds genuinely new context: it appends to the notebook's chat history, consumes NotebookLM quota, and returns references with real citation numbers and source IDs.

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?

Three short sentences, front-loaded with the core action and each carrying distinct information (action, side effect, cost, output detail). 'Sends the question to Google' is mildly redundant with the opening sentence, but overall it is tight.

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?

An output schema exists, so return-value explanation is not needed, and the description covers the key behavioral facts an agent needs: quota consumption, chat-history mutation, and citation-bearing references. The main gap is that the 'notebook' parameter's expected form (ID vs name) is never clarified.

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%, with two required parameters ('notebook', 'question') documented nowhere. The description references sending 'the question' and the 'notebook's chat history' but gives no format, identifier type, or constraints, so it does not compensate for the coverage gap.

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?

States a specific verb (Ask) and resource (Google NotebookLM about its sources), plus the side effect of appending to chat history. This clearly separates it from siblings like source_fulltext (raw text retrieval) and notebook_summary (summarization).

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

Usage Guidelines2/5

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

No explicit when-to-use or when-not-to-use guidance, and no sibling is named as an alternative. The description implies it answers questions grounded in notebook sources, but an agent must infer when to pick this over source_fulltext or notebook_summary.

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