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notebooklm-mcp

notebooklm-mcp

by gracelee087
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

studio_create

Generate an audio podcast or other Studio artifact from a NotebookLM notebook. Starts async generation, returns an artifact ID for tracking and downloading.

Instructions

Start generating a Studio artifact (audio/podcast by default).

Returns immediately with { artifact_id, status, title } — generation is asynchronous and typically takes 2-5 minutes. Poll download_artifact to check progress and retrieve the file when complete.

Currently implemented: audio (Audio Overview / podcast). Other artifact types (video, slides, etc.) raise NotImplementedError.

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>"
    },
    "name": "studio_create"
  }
}

Input Schema

NameRequiredDescriptionDefault
artifactNoaudio
languageNoen
notebook_idYes
instructionsNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.3/5.0
Behavior5/5

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

With no annotations, the description fully discloses the async behavior (returns immediately, 2-5 min generation), points to download_artifact for polling, and explicitly warns that non-audio artifact types raise NotImplementedError. This is transparent and sets correct expectations.

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?

Three sentences, front-loaded with the main action. Each sentence adds value: action, async/return/poll, and limitation. No redundancy.

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 behavior (create, async, poll, limitation) and return format. It is missing parameter definitions and explicit alternative guidance (studio_revise), but for a tool with an output schema, the description is largely sufficient.

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 must compensate. It only mentions the default audio artifact type; it does not explain notebook_id, language, or instructions. The schema provides some semantics (enum, defaults) but the description adds almost no parameter meaning.

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 opens with a specific verb+resource: 'Start generating a Studio artifact (audio/podcast by default).' It clearly distinguishes this from sibling studio_revise (revision) and download_artifact (retrieval), and explicitly notes the default artifact type and unimplemented types.

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?

It explains the async workflow: returns immediately, poll download_artifact to check progress and retrieve. This gives clear usage context. However, it does not explicitly contrast with studio_revise or state when NOT to use this tool (beyond the NotImplementedError for non-audio types). It also doesn't mention prerequisites like needing an existing notebook id.

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

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