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KHU Notebook Research Assistant

by drtagkim

KHU Notebook Research Assistant (MCP)

Version: 0.0.1 (2026-01-31)
Author: Taekyung Kim, PhD. Professor, Kyung Hee University

Overview

The KHU Notebook Research Assistant is a specialized Model Context Protocol (MCP) server designed to interface with Google NotebookLM. It empowers AI agents to conduct autonomous academic research, manage knowledge bases, and generate study artifacts efficiently.

This project is tailored for research and educational purposes, enabling seamless integration between LLMs and NotebookLM's grounded reasoning capabilities.

Related MCP server: Gemini Research MCP Server

Key Capabilities

  • Autonomous Deep Research: Performs multi-step web research and automatically summarizes and imports findings into your notebook.

  • Study Artifact Generation: Instantly creates derived materials from your sources:

    • Audio Overviews (Podcasts)

    • Research Reports

    • Infographics

    • Presentation Slides

    • Data Tables (Google Sheets)

    • Flashcards & Quizzes

    • Mind Maps

  • Knowledge Management: Systematic control over notebook creation, source addition (URLs, Text, PDFs), and organization.

Installation

npm install
npm run build

Configuration

This server requires a valid Google session to interact with NotebookLM. Session credentials (cookies) should be stored in ~/.notebooklm-mcp/auth.json or passed via environment variables.

Tools Available

Tool

Description

research_notebook_list

Lists all active research notebooks.

research_deep_search

Conducts deep web research on a specific topic.

generate_study_material

Generates artifacts like Slides, Reports, or Audio.

add_source_content

Injects new research materials (URLs, Text) into the notebook.

License

Private / Academic Use Only. Copyright (c) 2026 Taekyung Kim, PhD. All Rights Reserved.

Available Tools

6 tools
add_source_contentC

Add a specific source (text, url, pdf) to the notebook.

ParametersJSON Schema
NameRequiredDescriptionDefault
notebook_idYes
categoryYes
payloadYesContent or URL
titleNo

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool adds content but doesn't describe what happens during addition (e.g., whether it validates URLs, processes PDFs, requires authentication, has rate limits, or returns confirmation). This leaves significant gaps for a mutation tool with no output schema.

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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, with every word contributing to understanding the core functionality.

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?

Given the complexity (a mutation tool with 4 parameters, no annotations, and no output schema), the description is incomplete. It doesn't address behavioral aspects like error handling, response format, or integration with sibling tools. The agent lacks sufficient context to use this tool effectively beyond basic parameter passing.

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

Parameters3/5

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

Schema description coverage is low (25%), with only the 'payload' parameter having a description. The tool description adds minimal value by mentioning source types (text, url, pdf), which aligns with the 'category' enum, but doesn't explain parameter meanings beyond this. It doesn't clarify what 'notebook_id' refers to or how 'title' is used, leaving most parameters inadequately documented.

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?

The description clearly states the action ('Add') and resource ('specific source to the notebook'), specifying the types of sources (text, url, pdf). It distinguishes from sibling tools like research_notebook_create (which creates notebooks) and research_notebook_list (which lists notebooks), but doesn't explicitly differentiate from other content-related tools like generate_study_material.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing notebook), exclusions, or how it relates to sibling tools like generate_study_material or research_deep_search. The agent must infer usage from the tool name alone.

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

generate_study_materialC

Generate various study materials and artifacts.

ParametersJSON Schema
NameRequiredDescriptionDefault
notebook_idYes
material_typeYesType of material to generate
contextNoOptional configuration

TDQS

C2.6/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions generating materials but fails to describe critical traits like required permissions, whether it's a read or write operation, potential side effects, rate limits, or output format. This leaves significant gaps in understanding how the tool behaves in practice.

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?

The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It is front-loaded and appropriately sized for its content, though it could be more informative. There is no wasted verbiage, earning a high score for conciseness.

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?

Given the complexity of generating various study materials with 3 parameters, no annotations, and no output schema, the description is incomplete. It fails to explain what the tool returns, how it interacts with the notebook, or any behavioral nuances, making it inadequate for an agent to use the tool effectively without additional context.

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

Parameters3/5

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

The schema description coverage is 67% (2 out of 3 parameters have descriptions), with 'material_type' and 'context' documented but 'notebook_id' lacking a description. The tool description adds no additional meaning beyond the schema, such as explaining the purpose of 'notebook_id' or detailing 'context' options. Given the moderate coverage, a baseline score of 3 is appropriate as the schema does most of the work without compensation from the description.

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

Purpose3/5

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

The description states the tool 'Generate[s] various study materials and artifacts,' which provides a general purpose but lacks specificity about what resources it acts upon or how it differs from siblings like 'add_source_content' or 'research_notebook_create.' It's vague about the scope and mechanism, falling short of distinguishing itself clearly from related tools.

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 guidance is provided on when to use this tool versus alternatives such as 'add_source_content' or 'research_notebook_create.' The description implies usage for generating materials but offers no context, exclusions, or prerequisites, leaving the agent without direction on appropriate scenarios.

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

research_notebook_createC

Create a new research notebook.

ParametersJSON Schema
NameRequiredDescriptionDefault
titleYes

TDQS

C2.8/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states 'Create' which implies a write/mutation operation, but doesn't mention permissions needed, whether creation is idempotent, what happens on duplicate titles, or what the response contains. This leaves significant gaps for a mutation tool.

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 a single, efficient sentence with zero wasted words. It's appropriately sized for a simple creation tool and gets straight to the point without unnecessary elaboration.

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?

For a mutation tool with no annotations, 0% schema coverage, and no output schema, the description is inadequate. It doesn't explain what gets created, what the response might contain, error conditions, or how this tool fits with the sibling tools. The agent would have insufficient context to use this tool effectively.

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 the single parameter 'title' is completely undocumented in the schema. The description adds no information about this parameter - no format constraints, length limits, examples, or semantic meaning. It fails to compensate for the schema's lack of documentation.

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?

The description clearly states the verb ('Create') and resource ('research notebook'), making the purpose immediately understandable. It doesn't differentiate from sibling tools like 'research_notebook_list', but it's not misleading or tautological.

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?

The description provides no guidance on when to use this tool versus alternatives. There's no mention of prerequisites, when to choose this over other tools like 'add_source_content', or any context about its role in the workflow.

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

research_notebook_listB

List all available research notebooks.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

B3.2/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states 'List all available research notebooks' but doesn't describe what 'available' means, whether there are permissions or filters, how results are returned (e.g., pagination, format), or any rate limits. For a list operation with zero annotation coverage, this leaves significant behavioral gaps.

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 a single, efficient sentence that directly states the tool's purpose without any wasted words. It's appropriately sized for a simple list tool and front-loaded with the essential information. Every part of the sentence earns its place.

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

Completeness3/5

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), the description is adequate but has clear gaps. It covers the basic purpose but lacks details on behavioral aspects like what 'available' entails, return format, or usage context relative to siblings. Without annotations or output schema, the description should do more to compensate, making it minimally viable but incomplete.

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?

The tool has 0 parameters, and schema description coverage is 100% (empty schema is fully described). The description doesn't need to add parameter semantics beyond what the schema provides. Baseline for 0 parameters is 4, as there's nothing to compensate for, and the description correctly implies no inputs are required.

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?

The description clearly states the action ('List') and resource ('research notebooks'), making the tool's purpose immediately understandable. It doesn't differentiate from sibling tools like 'research_notebook_create', but it's not vague or tautological. The description accurately conveys what the tool does without restating just the name.

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?

The description provides no guidance on when to use this tool versus alternatives like 'research_deep_search' or 'add_source_content'. It doesn't mention prerequisites, context, or exclusions. While the purpose is clear, the lack of usage context leaves the agent without direction on when this is the appropriate choice among siblings.

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

update_credentialsC

Update session cookies manually.

ParametersJSON Schema
NameRequiredDescriptionDefault
cookiesYesRaw cookie string
csrf_tokenNo

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. 'Update session cookies' implies a mutation operation that modifies authentication state, but it doesn't describe what happens to existing cookies, whether this affects current sessions, or any security implications. The 'manually' qualifier adds some context but insufficient for a mutation tool.

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 a single, efficient sentence with zero wasted words. It's appropriately sized for a simple tool and front-loads the essential information.

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?

For a mutation tool with no annotations and no output schema, the description is inadequate. It doesn't explain what happens after updating cookies, whether the operation is reversible, what errors might occur, or how to verify success. The sibling tools appear unrelated, but this doesn't compensate for the missing behavioral context.

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

Parameters3/5

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

Schema description coverage is 50% (only 'cookies' has a description). The description doesn't add any parameter-specific information beyond what's in the schema. It doesn't explain what format the cookie string should be in, what CSRF tokens are used for, or relationships between parameters. Baseline 3 is appropriate given partial schema coverage.

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?

The description clearly states the action ('Update') and the resource ('session cookies'), and specifies 'manually' which adds useful context about the operation mode. It doesn't differentiate from sibling tools, but those appear unrelated to credential management, so this is adequate.

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?

The description provides no guidance on when to use this tool versus alternatives or prerequisites. It mentions 'manually' which hints at a specific use case but doesn't explain when manual updates are needed versus automated ones or what triggers this operation.

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

TDQS

B3/5.0
Disambiguation4/5

Most tools have distinct purposes: notebook creation/listing, content addition, research, and credential updates. However, 'generate_study_material' and 'research_deep_search' could overlap if study materials involve research findings, though descriptions suggest different focuses (generation vs. search/import).

Naming Consistency3/5

Naming is mixed: 'research_notebook_create' and 'research_notebook_list' follow a consistent pattern, but 'add_source_content' and 'generate_study_material' use different verb styles, and 'research_deep_search' and 'update_credentials' deviate further. The set lacks a uniform convention, though names remain readable.

Tool Count4/5

With 6 tools, the count is reasonable for a research assistant server, covering core notebook management and research tasks. It's slightly lean but not insufficient, as each tool appears to serve a specific function without obvious bloat.

Completeness3/5

The toolset covers notebook creation, listing, and content addition, but lacks update or delete operations for notebooks or sources, which could limit agent workflows. Research and generation tools are included, but gaps in lifecycle management may cause dead ends in complex tasks.

Maintenance

ActivityInactive
ResponsivenessSyncing

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