Skip to main content
Glama
DevstackK

Notebooklm-Unofficial-API-MCP

by DevstackK

source_list_play_books

Read-only

List Google Play Books eligible as NotebookLM sources, showing content IDs, titles, authors, and export status for adding purchased ebooks.

Instructions

List Google Play Books eligible to be added as sources (Expert Intelligence).

Returns the account's purchased ebooks NotebookLM can ingest (US only, 18+). Each row has content_id (pass it to source_add_play_book), title, authors, export_disabled and reason (a non-exportable title cannot be added), plus cover_url / store_url. Empty for an account with no Play Books library. Web backend only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
offsetNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds meaningful behavioral context beyond that: US-only, 18+ eligibility, that export_disabled/non-exportable titles cannot be added, empty result for no library, and web-backend-only. It omits pagination/rate behavior but otherwise goes well beyond the annotations.

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?

Front-loaded with purpose and eligibility, then the return fields and the follow-up action. Dense and information-rich with no filler; every clause (US only, 18+, export_disabled/reason, empty case) 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?

An output schema exists, so return-value detail is technically redundant, yet the description still names the row fields, which helps the agent chain to source_add_play_book. The only gap is the unexplained limit/offset parameters, which is minor for a standard paginated list.

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% and the description never mentions limit or offset. Both are standard pagination parameters an agent can infer, but with zero schema documentation and no compensating text, the definition does nothing to clarify their semantics or defaults.

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?

Specific verb ('List') + precise resource ('Google Play Books eligible to be added as sources'), with the NotebookLM/Expert Intelligence scope named. An agent can distinguish it from source_list (lists existing sources) and source_add_play_book (the follow-up action) without opening either schema.

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 description establishes clear context: this is the discovery step whose content_id feeds source_add_play_book, and it names that sibling explicitly. It doesn't spell out an explicit 'when not to use' or the full prerequisite chain, but the intended workflow is unambiguous.

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