Ebook-MCP
A Model Context Protocol server for conversational e-book interaction and AI integration.
Key Features
Use Cases
README
Ebook-MCP
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Ebook-MCP is a powerful Model Context Protocol (MCP) server for processing electronic books. Built on the Model Context Protocol, it provides a set of standardized APIs for seamless integration between LLM applications and e-book processing capabilities. Currently supports EPUB and PDF formats.
Use Cases & Value
Ebook-MCP transforms how you interact with your digital books by enabling natural language conversations with your reading materials. It seamlessly integrates with modern AI-powered IDEs like Cursor and Claude, allowing you to:
- Smart Library Management: Simply ask "Show me all EPUB files in my downloads folder" or "Find books about GenAI in my library"
- Interactive Reading Experience: Have natural conversations about your books:
- "Give me a brief introduction to 'LLM Engineer Handbook'"
- "What's covered in Chapter 3?"
- "Summarize the key points about RAG from this book"
- Active Learning Support: Enhance your learning through AI-powered interactions:
- "Create a quiz based on the RAG concepts from Chapter 5"
- "Explain the differences between the architectures discussed in this chapter"
- "Give me practical exercises based on the concepts in this section"
- Content Navigation: Easily navigate through your books with natural language queries:
- "Find all sections discussing prompt engineering"
- "Show me the chapters about fine-tuning"
- "Take me to the part about vector databases"
By bridging the gap between traditional e-books and AI capabilities, Ebook-MCP helps readers extract more value from their digital library through intuitive, conversation-based interactions.
Quick demo - Run with DeepSeek (Please check all the examples with Claude/OpenAI under mcp-client-example folder)
Query:Please check all the epub books in my download folder. user id is onebird.
......
Query: Can you ask me a few questions to test my understanding of this book?
Answer: Sure! Here are 5 questions about Elon Musk by Walter Isaacson to test your understanding of the book. Please answer based on your knowledge — I’ll give feedback and add details after each response.
Question 1:
How did Musk’s childhood and family background shape his personality and career later in life?
(Hint: His relationship with his father, growing up in South Africa, etc.)
Question 2:
Zip2 and X.com (later PayPal) were Musk’s early startups. What problems did they solve? How did Musk eventually exit from these companies?
(Hint: Zip2’s business model, X.com’s merger with Confinity, etc.)
Question 3:
In 2008, both SpaceX and Tesla were on the verge of bankruptcy. How did Musk save these two companies?
(Hint: Falcon 1’s fourth launch, NASA contract, Tesla’s last-minute rescue, etc.)
Question 4:
What controversies surround Musk’s management style? What specific incidents in the book illustrate his “hardcore” or “extreme” work culture?
(Hint: Fremont factory’s “production hell,” Twitter layoffs, etc.)
Question 5:
What is Musk’s attitude toward Artificial Intelligence (AI)? Why did he simultaneously support both OpenAI and Neuralink, which seem to have conflicting goals?
(Hint: AI as an existential threat, brain-machine interface vision, etc.)
Please answer each question. I’ll provide feedback and additional details or correct any misunderstandings!
(If a question isn’t clear, feel free to ask me.)
Query:
Question 1: His father’s abusive attitude made him less empathetic towards others.
Question 2: Zip2 solved the problem of electronic maps and online yellow pages. X.com solved online payments.
Question 3: He secured a contract with NASA. Tesla solved its production problems by building a factory in China.
Question 4: I don’t know. Please answer for me.
Question 5: He believes open-source technology is necessary to ensure AI is safe and open.
Answer: Your answers have captured some key points from the book. Let me add details, correct some inaccuracies, and provide further background for Questions 4 and 5:
Question 1 — Correction & Supplement
Influence of His Father:
Musk's father, Errol, was indeed extremely harsh — for example, forcing young Elon to kneel and count grains of rice for hours. The book emphasizes that this emotional desensitization shaped Musk into an extremely goal-oriented person — indifferent to others’ suffering but obsessively committed to his goals.
Reverse Impact:
It’s not that Musk is incapable of empathy — it’s selective. For instance, he may treat employees coldly but personally study medical equipment for his son X’s health issues.
Question 2 — Correction & Supplement
Zip2:
Essentially an online Yellow Pages + map navigation service (helping newspapers go online). Musk wrote much of the early code himself and slept in the office. Eventually, Compaq acquired Zip2 for $307 million (Musk received $22 million).
X.com:
Not just online payments — it was Musk’s attempt to create a comprehensive financial platform (aiming to replace banks). Later merged with Confinity (the precursor of PayPal). Musk was ousted due to internal conflicts, but when eBay acquired PayPal, Musk earned $180 million.
(Note: Musk later bought back the domain X.com — it’s now Twitter’s parent company name).
Question 3 — Correction & Supplement
SpaceX:
In 2008, Falcon 1 succeeded on its fourth launch (after three failures drained their funds). Right after that, SpaceX secured a $1.6 billion NASA contract for cargo resupply — a lifeline for the company.
Tesla:
In 2008, Tesla survived through a $50 million investment from Daimler and a loan from the U.S. Department of Energy.
Building the Gigafactory in China (2019) came much later. In the early years, survival depended on the launch of Model S (2012) and Musk’s personal gamble — selling all his real estate to fund Tesla.
Question 4 — Explanation: Controversy Around His Management Style
“Hardcore” Culture:
At SpaceX/Tesla: Musk demanded employees work 80+ hours per week, even sleeping at the factory. ("If you’re not here, your replacement is.")
After acquiring Twitter: He immediately laid off 75% of staff and asked the remaining employees to sign a "hardcore commitment" — agreeing to long hours and high-intensity output.
Extreme Cases:
At the Fremont Factory: During the "production hell" of the Model 3, Musk slept on the production line and once yelled at employees calling them "worse than dogs" (he later apologized).
Firing Executives: Musk once fired a senior Tesla executive on the spot because the font size in their PowerPoint presentation wasn’t big enough.
Question 5 — Correction & Supplement
AI Threat Theory:
Musk believes that AI could destroy humanity ("more dangerous than nuclear weapons"). Therefore:
OpenAI: He initially supported it to promote safe, open-source AI (but left later due to disputes over control).
Neuralink: He aims to use brain-machine interfaces to merge humans with AI — to prevent humans from being left behind.
Contradictions:
While warning about AI risks, Musk is simultaneously pushing for Full Self-Driving (FSD) technology at Tesla and has founded xAI to research Artificial General Intelligence (AGI).
Overall Evaluation:
✅ You have captured the key events accurately, but the details need to be strengthened (such as timelines and specific figures).
🔍 Recommended areas for further study: Musk's multi-threaded management style (running five companies simultaneously) and his skill in media manipulation (governing through Twitter).
Features
EPUB Support
- Extract metadata (title, author, publication date, etc.)
- Extract table of contents
- Extract chapter content (with Markdown output)
- Batch process EPUB files
PDF Support
- Extract metadata (title, author, creation date, etc.)
- Extract table of contents
- Extract content by page number
- Extract content by chapter title
- Markdown output support
- Batch process PDF files
Installation
- Clone the repository:
git clone https://github.com/yourusername/ebook-mcp.git
cd ebook-mcp
- Install dependencies using
uv:
uv pip install -e .
Usage
Starting the MCP Server in development mode
Run the server in development mode:
uv run mcp dev src/ebook_mcp/main.py
You can visit http://localhost:5173/ for testing & debugging purpose You can also install the inspector for the test.
npx @modelcontextprotocol/inspector uv --directory . run src/ebook_mcp/main.py
Starting the MCP Server in Prod mode
Run the server:
uv run src/ebook_mcp/main.py
Configure the MCP in Cursor
Add the following configuration in Cursor
"ebook-mcp":{
"command": "uv",
"args": [
"--directory",
"/Users/onebird/github/ebook-mcp/src/ebook_mcp/",
"run",
"main.py"
]
}
EPUB Processing Examples
# Get all EPUB files in a directory
epub_files = get_all_epub_files("/path/to/books")
# Get EPUB metadata
metadata = get_metadata("/path/to/book.epub")
# Get table of contents
toc = get_toc("/path/to/book.epub")
# Get specific chapter content (in Markdown format)
chapter_content = get_chapter_markdown("/path/to/book.epub", "chapter_id")
PDF Processing Examples
# Get all PDF files in a directory
pdf_files = get_all_pdf_files("/path/to/books")
# Get PDF metadata
metadata = get_pdf_metadata("/path/to/book.pdf")
# Get table of contents
toc = get_pdf_toc("/path/to/book.pdf")
# Get specific page content
page_text = get_pdf_page_text("/path/to/book.pdf", 1)
page_markdown = get_pdf_page_markdown("/path/to/book.pdf", 1)
# Get specific chapter content
chapter_content, page_numbers = get_pdf_chapter_content("/path/to/book.pdf", "Chapter 1")
API Reference
EPUB APIs
get_all_epub_files(path: str) -> List[str]
Get all EPUB files in the specified directory.
get_metadata(epub_path: str) -> Dict[str, Union[str, List[str]]]
Get metadata from an EPUB file.
get_toc(epub_path: str) -> List[Tuple[str, str]]
Get table of contents from an EPUB file.
get_chapter_markdown(epub_path: str, chapter_id: str) -> str
Get chapter content in Markdown format.
PDF APIs
get_all_pdf_files(path: str) -> List[str]
Get all PDF files in the specified directory.
get_pdf_metadata(pdf_path: str) -> Dict[str, Union[str, List[str]]]
Get metadata from a PDF file.
get_pdf_toc(pdf_path: str) -> List[Tuple[str, int]]
Get table of contents from a PDF file.
get_pdf_page_text(pdf_path: str, page_number: int) -> str
Get plain text content from a specific page.
get_pdf_page_markdown(pdf_path: str, page_number: int) -> str
Get Markdown formatted content from a specific page.
get_pdf_chapter_content(pdf_path: str, chapter_title: str) -> Tuple[str, List[int]]
Get chapter content and corresponding page numbers by chapter title.
Dependencies
Key dependencies include:
- ebooklib: EPUB file processing
- PyPDF2: Basic PDF processing
- PyMuPDF: Advanced PDF processing
- beautifulsoup4: HTML parsing
- html2text: HTML to Markdown conversion
- pydantic: Data validation
- fastmcp: MCP server framework
Important Notes
- PDF processing relies on the document's table of contents. Some features may not work if TOC is not available.
- For large PDF files, it's recommended to process by page ranges to avoid loading the entire file at once.
- EPUB chapter IDs must be obtained from the table of contents structure.
Architecture
┌────────────────────────────┐
│ Agent Layer │
│ - Translation Strategy │
│ - Style Consistency Check │
│ - LLM Call & Interaction │
└────────────▲─────────────┘
│ Tool Calls
┌────────────┴─────────────┐
│ MCP Tool Layer │
│ - extract_chapter │
│ - write_translated_chapter│
│ - generate_epub │
└────────────▲─────────────┘
│ System/IO Calls
┌────────────┴─────────────┐
│ System Base Layer │
│ - File Reading │
│ - ebooklib Parsing │
│ - File Path Storage/Check│
└────────────────────────────┘
Contributing
We welcome Issues and Pull Requests!
For detailed information about recent changes, please see CHANGELOG.md.
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