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Morfeu333

NotebookLM MCP Server

by Morfeu333

notebook_describe

Generate AI summaries and topic suggestions for NotebookLM notebooks to quickly understand content structure and key themes.

Instructions

Get AI-generated notebook summary with suggested topics.

Args: notebook_id: Notebook UUID

Returns: summary (markdown), suggested_topics list

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
notebook_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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 states the tool 'Get's data (implying a read operation) and returns specific outputs, but lacks details on permissions, rate limits, or whether it's idempotent. For a tool with zero annotation coverage, this is insufficient for safe invocation.

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?

The description is front-loaded with the core purpose, followed by clear sections for args and returns, making it efficient and well-structured. However, the 'Args' and 'Returns' labels are slightly redundant since the schema and output schema cover this, but they don't significantly detract from conciseness.

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 low complexity (one parameter), the presence of an output schema (which handles return values), and no annotations, the description is reasonably complete. It covers the purpose, parameter semantics, and return structure, though it could benefit from more behavioral context to fully compensate for the lack of annotations.

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 adds meaningful context beyond the schema: it specifies that 'notebook_id' is a 'Notebook UUID', clarifying the format and purpose of the single parameter. With 0% schema description coverage and only one parameter, this adequately compensates, though it could note if the UUID must be valid or from a specific source.

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 description clearly states the verb 'Get' and the resource 'AI-generated notebook summary with suggested topics', making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'notebook_get' or 'notebook_query', which likely retrieve raw notebook data rather than AI-generated summaries.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'notebook_get' or 'notebook_query'. It mentions what it returns but doesn't specify use cases, prerequisites, or exclusions, leaving the agent to infer usage from context alone.

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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