NotebookLM MCP Server

by Robiton

Overview Schema Source Related Servers Discussions Admin

Python

Remote

notebook_query_status

Check the status of an asynchronous notebook query and retrieve the result once it is completed.

Instructions

Check the status of an async notebook query started with notebook_query_start.

Returns the query result when completed, or current status if still in progress. Poll this tool every few seconds until status is 'completed' or 'error'.

Args: query_id: The query ID returned by notebook_query_start

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": {
      "query_id": "<query_id>"
    },
    "name": "notebook_query_status"
  }
}

Input Schema

Name	Required	Description	Default
`query_id`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.8May 28, 2026

TDQS

A4.3/5.0

Behavior3/5

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

No annotations provided, so description carries full burden. It discloses that the tool returns query result or current status, and mentions polling behavior. However, it lacks details on error handling, side effects, or rate limits. Adequate but not comprehensive.

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?

The description is concise and well-structured: a clear purpose statement, behavioral note, polling instruction, and args section. Every sentence adds value, no fluff.

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

Completeness5/5

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

Given the presence of an output schema (not shown but indicated), the description adequately covers the tool's behavior. It explains return values (result or status) and polling usage. No gaps for a simple polling tool.

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

Parameters4/5

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

Only one parameter (query_id) with 0% schema description coverage. The description adds meaning by stating 'The query ID returned by notebook_query_start', linking it to the start tool. This compensates for the schema's lack of description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Check the status of an async notebook query started with notebook_query_start', specifying the resource (async notebook query) and action (check status). It distinguishes from sibling tools like notebook_query_start which initiates the query.

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

Usage Guidelines4/5

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

Provides explicit polling guidance: 'Poll this tool every few seconds until status is 'completed' or 'error'.' This indicates when and how to use it after starting a query. No explicit alternatives or when-not, but the advice is clear.

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