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Morfeu333

NotebookLM MCP Server

by Morfeu333

source_get_content

Extract raw text content from PDFs, web pages, pasted text, or YouTube transcripts for content export without AI processing.

Instructions

Get raw text content of a source (no AI processing).

Returns the original indexed text from PDFs, web pages, pasted text, or YouTube transcripts. Much faster than notebook_query for content export.

Args: source_id: Source UUID

Returns: content (str), title (str), source_type (str), char_count (int)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
source_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does well by stating the tool returns 'original indexed text' and is 'much faster' than alternatives, but doesn't mention potential limitations like file size constraints, rate limits, or authentication requirements that would be helpful for a read operation.

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 perfectly structured and front-loaded: first sentence states purpose, second explains performance advantage, third lists supported sources, then clearly separated Args and Returns sections. Every sentence earns its place with no wasted words.

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?

For a single-parameter read operation with an output schema (implied by the Returns section), the description provides excellent completeness. It covers purpose, usage guidance, performance characteristics, parameter semantics, and return values - everything needed for effective tool selection and invocation.

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

Parameters4/5

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

The description explicitly documents the single parameter 'source_id' as a 'Source UUID', adding crucial semantic meaning beyond the schema's basic string type. With 0% schema description coverage and only one parameter, this provides excellent compensation and clarity.

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 specific action ('Get raw text content'), target resource ('of a source'), and scope ('no AI processing'). It explicitly distinguishes from sibling 'notebook_query' by stating it's 'much faster' for content export, providing clear differentiation.

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?

The description provides explicit guidance on when to use this tool ('for content export') and when to use an alternative ('much faster than notebook_query'). It also implies usage context by listing supported source types (PDFs, web pages, etc.).

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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