Skip to main content
Glama

notebooklm_prepare_content

Convert and split content for NotebookLM upload by transforming markdown to plain text and dividing large files into manageable chunks under 500KB limits.

Instructions

Prepare content for NotebookLM upload.

Converts and splits content to meet NotebookLM requirements:

  • Converts markdown to plain text if needed

  • Splits large files into ~400KB chunks (under 500KB limit)

  • Creates numbered part files

Returns paths to prepared files ready for upload.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
input_pathYesPath to the input file
output_dirNoDirectory for output files (default: same as input)
max_size_kbNoMaximum size per chunk in KB (default: 400)
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: conversion (markdown to plain text), splitting (into ~400KB chunks), and file creation (numbered part files). However, it lacks details on error handling, performance characteristics, or what happens if input doesn't meet requirements. The description doesn't contradict any 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?

The description is well-structured and front-loaded with the main purpose. Each sentence adds value: the first states the overall goal, the next explains the conversion and splitting process, and the final sentence describes the return value. There is no wasted text.

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?

Given the tool's complexity (file processing with conversion and splitting) and the absence of both annotations and an output schema, the description does a good job explaining what the tool does and what it returns ('paths to prepared files'). However, it could be more complete by detailing error cases or the format of returned paths.

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 description coverage is 100%, so the schema already documents all three parameters. The description adds some context by mentioning '~400KB chunks' which relates to max_size_kb, but doesn't provide additional meaning beyond what's in the schema descriptions. The baseline of 3 is appropriate when schema coverage is high.

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 clearly states the tool's purpose with specific verbs ('Prepare content for NotebookLM upload', 'Converts and splits content') and identifies the resource ('content', 'files'). It distinguishes this tool from sibling tools by focusing on content preparation rather than authentication, listing, or uploading operations.

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 provides clear context for when to use this tool ('Prepare content for NotebookLM upload'), implying it should be used before uploading. However, it does not explicitly state when NOT to use it or name specific alternatives among the sibling tools (e.g., notebooklm_upload_sources might be the next step).

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Shigakuresama/canvas-mcp-developer'

If you have feedback or need assistance with the MCP directory API, please join our Discord server