NotebookLM MCP Server

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 describes the core behavior (querying existing notebook sources with AI) but lacks details on permissions, rate limits, response format, or error conditions. It adds some context about default behavior for source_ids but is otherwise minimal.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured with a clear purpose statement, usage guidelines, and parameter explanations in bullet points. Every sentence adds value without redundancy, and it's front-loaded with the most important information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (4 parameters, no annotations, but with an output schema), the description covers purpose, usage, and parameters adequately. The output schema likely handles return values, so the description doesn't need to explain those. It could benefit from more behavioral details but is largely complete.

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

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

Schema description coverage is 0%, but the description provides meaningful semantics for all 4 parameters: notebook_id (Notebook UUID), query (Question to ask), source_ids (Source IDs to query with default behavior), and conversation_id (For follow-up questions). This compensates well for the lack of schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

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 ('Ask AI about EXISTING sources already in notebook') and distinguishes it from sibling tools by explicitly contrasting with 'research_start' for finding new sources. It precisely defines the scope and resource.

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

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 ('Ask AI about EXISTING sources already in notebook') and when not to use it ('NOT for finding new sources'), with a named alternative ('Use research_start instead for: deep research, web search, find new sources, Drive search'). This clearly differentiates it from sibling tools.

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