Notebooklm-Unofficial-API-MCP

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chat_ask

Ask a notebook's sources questions and get cited answers, or recall prior conversation turns with follow-up suggestions.

Instructions

Ask a notebook's sources a question, and/or recall prior turns. Accepts a notebook name or ID.

Pass conversation_id to continue a specific conversation; omit it to continue the notebook's most-recent conversation (or start a new one).

source_ids (optional) scopes the question to specific sources by id/prefix/title; omit it to query every source. It accepts a real list, a JSON-array string, or a comma-separated string (the comma form cannot carry a source title that itself contains a comma — use a JSON array or a real list for those).

history (optional, default 0): the max number of prior Q&A pairs (each a {question, answer}) to also return (oldest-first), from the conversation as it stood before this question. There is no unbounded "all" value — pass a generously large number (e.g. 100) for the whole conversation. Omit question (leave it empty) with history > 0 to recall prior pairs without asking anything new; a recall-only call also echoes the conversation_id it read. Pass neither and the call is rejected.

Returns the answer, turn_number, and citation references (when a question is asked). references controls citation detail: lite (default) returns source_id / citation_number / cited_text; full adds chunk-level char offsets and scores.

suggest_followups (optional, default False): when True the result also carries a suggested_prompts list of AI-suggested follow-up questions (each a {title, prompt}), scoped to the same source_ids and steered by question when one is given. It works on its own too — pass it with no question (and history 0) to get suggested questions without asking anything. When omitted/False the result never contains a suggested_prompts key.

Input Schema

TableJSON Schema

Name	Required	Default
`history`	No
`notebook`	Yes
`question`	No
`references`	No	lite
`source_ids`	No
`conversation_id`	No
`suggest_followups`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 5, 2026

TDQS

A4.5/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses the rejection condition, the absence of an unbounded history value, the conditional presence of suggested_prompts, and the citation detail levels. It does not mention permission/auth requirements or any rate limits, so a small gap remains.

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?

Long but front-loaded and organized by parameter, with no filler sentences. The final suggest_followups paragraph is dense and slightly sprawling, and some return-value detail duplicates the output schema, but nearly every clause conveys non-obvious behavior.

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

Completeness5/5

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

Given 7 parameters, 0% schema coverage, no annotations, and an existing output schema, the description covers all invocation-relevant details: defaults, mutual-exclusion rules, mode switching, and error conditions. An agent could call this correctly without further documentation.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate, and it does for every parameter: notebook (name or ID), source_ids formats and the comma-in-title caveat, history semantics and the lack of an 'all' value, question-omission behavior, and the exact fields returned by references=lite vs full.

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 opening sentence names a concrete verb and resource ('Ask a notebook's sources a question, and/or recall prior turns') and states accepted identifiers, so the core operation is unambiguous. It does not, however, differentiate itself from siblings like suggest_prompts or chat_start, which overlap with the recall/follow-up modes described later.

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 pass conversation_id vs omit it, when source_ids scoping applies vs omitting to query every source, how history interacts with question (recall-only mode), and the failure case ('Pass neither and the call is rejected'). These are exactly the decision rules an agent needs before invoking.

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