NotebookLM MCP Server (Security Hardened)

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

Annotations already cover key traits (read-only, non-destructive, idempotent, closed-world), but the description adds valuable context: it returns metadata fields (name, topics, use cases, URL) and implies a use case for selection workflows. No contradictions with annotations, and it enhances understanding beyond structured data.

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?

Two sentences that are front-loaded with core functionality and followed by usage guidance. Every word earns its place—no redundancy or fluff—making it efficient and easy to parse for an AI agent.

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 simplicity (0 parameters, no output schema) and rich annotations, the description is complete: it explains what it does, when to use it, output details, and distinguishes from siblings. No gaps remain for effective agent use.

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 parameters and 100% schema description coverage, the baseline is high. The description adds no parameter details (as there are none), but it clarifies the output scope (metadata fields) and purpose, which compensates for the lack of an output schema, providing useful semantic context.

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 verb ('List') and resource ('all library notebooks'), specifies the metadata included (name, topics, use cases, URL), and distinguishes it from siblings like 'search_notebooks' by emphasizing comprehensive listing without filtering. This provides specific, actionable intent.

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?

It explicitly states when to use this tool ('to present options') and provides a clear workflow ('then ask which notebook to use for the task'), distinguishing it from alternatives like 'search_notebooks' for filtered queries. This gives strong guidance on context and next steps.

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