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 AI about existing sources) and mentions a default behavior ('source_ids: default: all') and follow-up capability ('conversation_id: For follow-up questions'). However, it lacks details on permissions, rate limits, error conditions, or what the AI response entails. For a tool with no annotations, this is adequate but leaves gaps in behavioral understanding.

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 front-loaded with the core purpose and usage guidelines, followed by a clear Args section. Every sentence earns its place: the first defines scope and exclusions, the second names an alternative, and the parameter explanations are succinct. No wasted words, making it easy to scan and understand.

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 no annotations, 0% schema coverage, but an output schema exists, the description is mostly complete. It covers purpose, usage, and parameter semantics well. The output schema handles return values, so the description doesn't need to explain those. However, for a tool with no annotations, it could better address behavioral aspects like error handling or authentication needs.

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%, so the description must compensate. It adds meaningful semantics for all four parameters: notebook_id as 'Notebook UUID', query as 'Question to ask', source_ids with default behavior and optional filtering, and conversation_id for context in follow-ups. This goes beyond the bare schema, though it could provide more detail on format constraints (e.g., UUID structure).

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: 'Ask AI about EXISTING sources already in notebook.' It specifies the verb ('Ask AI about'), resource ('EXISTING sources'), and scope ('already in notebook'), and explicitly distinguishes it from sibling tools by stating 'NOT for finding new sources.' This provides excellent differentiation from alternatives like research_start.

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 usage guidelines: 'NOT for finding new sources' and 'Use research_start instead for: deep research, web search, find new sources, Drive search.' It clearly defines when to use this tool (querying existing notebook sources) versus when to use an alternative (research_start for finding new sources), offering comprehensive guidance.

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