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notebooklm

notebooklm

by dfelix-net
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

ask_notebook

Ask a question and get an answer grounded in a notebook's sources, so you can query research material without leaving your workflow.

Instructions

Faz uma pergunta respondida com base nas fontes do caderno.

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_id": "<notebook_id>",
      "question": "<question>"
    },
    "name": "ask_notebook"
  }
}

Input Schema

NameRequiredDescriptionDefault
questionYes
notebook_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.7/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It does not say whether the call is read-only, whether the answer cites sources, whether the notebook must already contain sources, or what happens if it does not. Only the answering basis ('fontes do caderno') is disclosed.

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

Conciseness4/5

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

A single tight sentence with no filler, front-loading the action. It is efficient, though its brevity reflects under-specification rather than deliberate economy.

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

Completeness2/5

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

With no annotations, no output schema, and 0% parameter description coverage, this two-parameter tool needs the description to explain prerequisites and return behavior. The single sentence leaves the agent without enough to invoke it confidently beyond guessing.

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?

Schema description coverage is 0%, so both parameters (notebook_id, question) are undocumented in the schema. The description only loosely implies that the question is answered against notebook sources; it adds no format, length, or ID semantics to compensate for the coverage 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 ('Faz uma pergunta') and resource ('fontes do caderno'), making clear this is a question-answering tool scoped to notebook sources. It implicitly distinguishes itself from the generation-oriented siblings (mind map, quiz, audio), but never names an alternative explicitly.

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

Usage Guidelines2/5

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

No when-to-use guidance, no prerequisites, and no mention of when a different tool (e.g. get_notebook_summary) would be preferable. The agent must infer usage entirely from the purpose statement.

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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