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NotebookLM Retrieval MCP

NotebookLM Retrieval MCP

by dillettante
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

notebook_summary

Read-onlyIdempotent

Retrieve a NotebookLM notebook's AI-generated summary to quickly review its derived content without accessing the original sources.

Instructions

Read NotebookLM's AI-generated notebook summary; it is derived content, not the original source.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "notebook": "<notebook>"
    },
    "name": "notebook_summary"
  }
}

Input Schema

NameRequiredDescriptionDefault
notebookYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds a genuinely useful behavioral caveat about the content's provenance (AI-generated, not primary source), but says nothing about what happens if the summary has not been generated or how fresh it is.

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

Conciseness5/5

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

A single dense sentence with zero filler; the core action is front-loaded and the provenance caveat follows immediately.

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?

An output schema exists, so return values need not be described, and the safety annotations are present. The main remaining gap is that the lone parameter's expected format is undocumented in both schema and description.

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

Parameters2/5

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

There is one required parameter ('notebook') with 0% schema description coverage, and the description does not clarify whether it expects a notebook ID, title, or URL. It fails to compensate for the complete documentation gap.

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?

States a specific verb and resource ('Read ... notebook summary') and adds the qualifier that the content is AI-generated derived material rather than the original source, which distinguishes it from the source_fulltext sibling without naming it.

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

Usage Guidelines3/5

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

The phrase 'derived content, not the original source' implies you should use this when a synthetic summary suffices rather than when you need original text, which is implied guidance toward source_fulltext. However, no alternative is named and no explicit when-not condition or prerequisite is stated.

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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