KHU Notebook Research Assistant
Interfaces with Google NotebookLM to enable autonomous academic research, knowledge management, and the generation of research artifacts like audio overviews and reports.
Allows for the generation of research data tables directly within Google Sheets.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@KHU Notebook Research AssistantConduct deep research on renewable energy and generate a research report"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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 buildConfiguration
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 |
| Lists all active research notebooks. |
| Conducts deep web research on a specific topic. |
| Generates artifacts like Slides, Reports, or Audio. |
| 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 toolsadd_source_contentC
Add a specific source (text, url, pdf) to the notebook.
| Name | Required | Description | Default |
|---|---|---|---|
| notebook_id | Yes | ||
| category | Yes | ||
| payload | Yes | Content or URL | |
| title | No |
TDQS
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.
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.
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.
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.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| notebook_id | Yes | ||
| material_type | Yes | Type of material to generate | |
| context | No | Optional configuration |
TDQS
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.
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.
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.
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.
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.
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_deep_searchC
Conduct deep web research and import findings automatically.
| Name | Required | Description | Default |
|---|---|---|---|
| notebook_id | Yes | ||
| topic | Yes | Research topic/query |
TDQS
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 automation but lacks details on permissions, rate limits, data sources, or what 'import findings' entails (e.g., format, storage location). This is inadequate for a tool that likely involves external web access and data mutation.
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?
The description is a single, efficient sentence with zero waste. It's front-loaded with the core action and outcome, making it easy to parse quickly.
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 complexity of web research and data import, with no annotations, no output schema, and incomplete parameter documentation, the description is insufficient. It doesn't cover behavioral aspects like error handling, data formats, or integration with sibling tools, leaving significant gaps for agent usage.
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?
Schema description coverage is 50% (only 'topic' has a description). The description adds no parameter-specific information beyond implying 'topic' is for research queries and 'notebook_id' might be for output storage. It partially compensates but doesn't fully address the undocumented 'notebook_id' parameter.
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 action ('conduct deep web research and import findings') and resource ('findings'), specifying it's automated. However, it doesn't distinguish this from sibling tools like 'add_source_content' or 'generate_study_material', which might have overlapping functionality with research operations.
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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context, or exclusions, such as how it differs from 'research_notebook_create' or 'add_source_content' for handling research data.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes |
TDQS
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.
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.
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.
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.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
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.
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.
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.
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.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| cookies | Yes | Raw cookie string | |
| csrf_token | No |
TDQS
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.
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.
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.
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.
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.
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
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 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.
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.
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.
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