NotebookLM MCP Server

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

With no annotations provided, the description carries full burden for behavioral disclosure. It describes the core functionality well but lacks details about permissions needed, rate limits, error conditions, or what the AI response format looks like. The description doesn't contradict any annotations (none exist), but could provide more behavioral context for a tool that queries AI about sources.

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 purpose statement first, exclusion guidance second, alternative tool reference third, and parameter explanations in a clean Args section. Every sentence earns its place with no wasted words or redundancy.

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 has an output schema (which handles return values), no annotations, and 4 parameters with good semantic coverage in the description, the description is mostly complete. It could benefit from more behavioral context about the AI interaction, but covers purpose, usage guidelines, and parameters effectively for a query tool.

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?

With 0% schema description coverage, the description compensates well by explaining all 4 parameters in the Args section. It clarifies that notebook_id is a 'Notebook UUID', query is a 'Question to ask', source_ids 'default: all', and conversation_id is 'For follow-up questions'. This adds meaningful context beyond the bare schema.

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 explicitly distinguishes it from sibling tools by stating 'NOT for finding new sources' and naming the alternative 'research_start'. This provides excellent differentiation from related tools.

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'), plus specifically names the alternative tool 'research_start' for different use cases. This gives the agent clear decision criteria.

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