CalibreMCP 📚

FastMCP 3.1+ server for Calibre e-book library management with sampling, agentic workflows, skills, prompts, and LanceDB metadata RAG

Features:

• FastMCP 3.1 - Sampling, agentic tool chaining, optional SearchTools/CodeMode transforms
• Metadata RAG (LanceDB) - Semantic search over title, authors, tags, comments via calibre_metadata_index_build and calibre_metadata_search; preparation for full-book text RAG (see docs/FULL_TEXT_RAG_DESIGN.md)
• Skills & Prompts - Registered prompts and skill workflows (reading recommendations, library health, semantic search, agentic guide)
• Neural Media RAG & DeepIngestor - Full-text LanceDB retrieval via calibre_rag and rag_retrieve when index is built
• Webapp - Full browser UI: sidebar nav, books/authors/series/tags, Semantic Search (RAG), Skills, Agentic pages, AI chat (Ollama/LM Studio), import/export
• Calibre Plugin - Edit extended metadata (translator, first_published, user comments) in Calibre GUI; VL from query
• Portmanteau Tools - 21 consolidated tools (18 portmanteau + 3 specialized)
• Windows Compatibility - ✅ Fixed Unicode encoding issues, starts reliably on Windows
• Standardized Documentation - 100% compliance with docstring standards
• Default Library Auto-Loading - No manual library setup needed
• Natural Language Search - Intelligent parsing via FastMCP 3.1 sampling and agentic workflows (SEP-1577)
• Auto-Open Books - ✅ Unique search results automatically launch in system viewer
• Title-Specific Search - ✅ Fast, exact title matching with title parameter
• Comprehensive Search - All verbs (search, list, find, query, get) work seamlessly
• Verb Mapping - Claude automatically maps user queries to correct operations
• Comment Management - Dedicated CRUD operations for book comments
• Random Book Opening - Open random books by author/tag/series with manage_viewer(operation="open_random")
• Metadata Display - Show comprehensive book metadata with formatted HTML popup via manage_metadata(operation="show")

Austrian efficiency for Sandra's 1000+ book digital library. Built with realistic AI-assisted development timelines (days, not weeks).

🚀 Quick Start

Prerequisites

• Calibre 6.0+ installed and running
• Calibre Content Server enabled on port 8080 (optional, for remote access)
• Node.js (for MCPB package installation)
• Python 3.11+ (for development/standard install)

Understanding Calibre Library Access Methods

CalibreMCP supports multiple ways to access your Calibre library, each with different use cases:

1. Direct Database Access (Local Libraries) ✅ Supported - Primary Method

What it is: Direct read/write access to Calibre's SQLite database (metadata.db)

How it works:

• CalibreMCP directly opens and queries the SQLite database file
• No Calibre application or server needs to be running
• Fast, efficient, and supports all operations

When to use:

• ✅ Default method - Works automatically when you provide a library path
• ✅ Local libraries on the same machine
• ✅ Best performance for read operations
• ✅ Full read/write access to metadata

Configuration:

• Set calibre_library_path to your Calibre library directory
• No additional setup required

Example:

{
  "mcpServers": {
    "calibre-mcp": {
      "env": {
        "CALIBRE_LIBRARY_PATH": "L:/Multimedia Files/Written Word/Calibre-Bibliothek"
      }
    }
  }
}

2. Calibre Content Server API ✅ Supported - Remote Access

What it is: HTTP API provided by Calibre's Content Server (calibre-server)

How it works:

• Uses Calibre's built-in Content Server (not the web interface)
• Communicates via HTTP/REST API
• Requires Calibre Content Server to be running

When to use:

• ✅ Remote libraries (different machine/network)
• ✅ When you want to access library over network
• ✅ When Calibre GUI is running on another machine

Configuration:

• Start Calibre Content Server: calibre-server --port=8080
• Set CALIBRE_SERVER_URL environment variable
• Authentication: Optional for read-only access, required for write/edit operations

⚠️ Important Authentication Note:

• Read operations (search, list, get details) work without authentication
• Write/Edit operations (update metadata, add books, delete) require authentication
• If you need to modify your library remotely, you must enable authentication

Remote Access Setup:

For accessing Calibre Content Server on a remote machine, you have two options:

Option A: Port Forwarding (SSH/Network)

# On the machine running Calibre Content Server
calibre-server --port=8080

# On your local machine, set up SSH port forwarding
ssh -L 8080:localhost:8080 user@remote-machine

# Then use localhost in your config
# CALIBRE_SERVER_URL: "http://localhost:8080"

Option B: Tailscale (Recommended for Secure Remote Access)

# On the machine running Calibre Content Server
calibre-server --port=8080 --listen-on=0.0.0.0

# On Tailscale network, use the Tailscale IP
# CALIBRE_SERVER_URL: "http://100.x.x.x:8080" (your Tailscale IP)

⚠️ Security Note: When exposing Calibre Content Server over a network:

• Read-only access works without authentication (for browsing/searching)
• Write/edit operations require authentication - enable auth if you need to modify the library
• Use Tailscale or VPN for secure access (recommended)
• Avoid exposing directly to the internet without proper security

Example Configuration:

{
  "mcpServers": {
    "calibre-mcp": {
      "env": {
        "CALIBRE_SERVER_URL": "http://localhost:8080",
        "CALIBRE_USERNAME": "optional_username",
        "CALIBRE_PASSWORD": "optional_password"
      }
    }
  }
}

Start Calibre Content Server:

# Option 1: Through Calibre GUI
# Calibre → Connect/share → Start Content Server

# Option 2: Command line (local only)
calibre-server --port=8080

# Option 3: Command line (network accessible - use with Tailscale/VPN)
calibre-server --port=8080 --listen-on=0.0.0.0

# With authentication (required for write/edit operations, optional for read-only)
calibre-server --port=8080 --listen-on=0.0.0.0 --enable-auth --manage-users

3. Calibre Web Server ❌ Not Supported

What it is: Calibre's web-based user interface (different from Content Server)

Status: CalibreMCP does not support the Calibre Web Server interface. We use the Content Server API instead.

Why not supported:

• Web Server is designed for human interaction, not API access
• Content Server provides a proper REST API for programmatic access
• Direct database access is more efficient for local libraries

4. Calibre CLI Tools ℹ️ Available but Not Used by CalibreMCP

What it is: Calibre provides command-line tools for library management

Available CLI Tools:

• calibredb - Database management (add, remove, export books, manage metadata)
• ebook-convert - Format conversion
• ebook-meta - Metadata reading/writing
• calibre-server - Content Server (used by CalibreMCP)

Status: CalibreMCP does not use these CLI tools. We use:

• Direct SQLite access for local libraries (faster, more reliable)
• Content Server API for remote libraries (proper API, not CLI)

Why not used:

• Direct database access is faster than CLI tools
• CLI tools require subprocess execution (slower, error-prone)
• API access is more reliable and provides better error handling

Note: You can still use Calibre CLI tools independently if needed:

# List books
calibredb list

# Convert format
ebook-convert book.epub book.pdf

# Get metadata
ebook-meta book.epub

5. Calibre Plugin (Optional) - GUI Integration

A Calibre plugin provides GUI integration for extended metadata and webapp features.

Features:

• Edit first_published, translator, user comments (Ctrl+Shift+M)
• Create virtual libraries from search queries (requires webapp backend)
• Syncs with calibre_mcp_data.db - no MCP process needed for metadata

Install: calibre-customize -b calibre_plugin or calibre-customize -a calibre_mcp_integration.zip

See docs/integrations/CALIBRE_PLUGIN_DESIGN.md.

6. Webapp (Optional) - Browser Interface

A Next.js webapp provides a full browser UI for the Calibre library.

Features:

• Retractable sidebar: Overview, Libraries, Books, Search, Authors, Series, Tags, Publishers, Import, Export, Chat, Logs, Settings, Help
• AI Chat (Ollama, LM Studio, OpenAI-compatible) with personality presets and Settings
• Book modal with full metadata (rating, publisher, series, identifiers, comments/description), Read button, author Wikipedia links
• Logs page: Log file viewer with tail, filter, level filter, live tail (polling with backoff), plus System status view
• Import (add by path), Export (CSV/JSON)

Start: Use webapp\start.ps1 for reservoir ports (10720 backend, 10721 frontend) with kill-port zombie clear. Or legacy: cd webapp/backend; uvicorn app.main:app --port 13000 and cd webapp/frontend; npm run dev
See webapp/README.md, webapp/SETUP.md (port rules, zombie kill).

7. Logging

• Stdio mode (python -m calibre_mcp): Writes to logs/calibremcp.log with rotation (10MB, 5 backups)
• Webapp backend: Writes to logs/webapp.log with rotation
• Log files are tail-able via the webapp Logs page

---

Start Calibre Content Server (if using remote access)

# Option 1: Through Calibre GUI
# Calibre → Connect/share → Start Content Server

# Option 2: Command line
calibre-server --port=8080

# With authentication
calibre-server --port=8080 --enable-auth --manage-users

---

🚀 Installation

Prerequisites

• uv installed (RECOMMENDED)
• Python 3.12+

📦 Quick Start

Run immediately via uvx:

uvx calibre-mcp

🎯 Claude Desktop Integration

Add to your claude_desktop_config.json:

"mcpServers": {
  "calibre-mcp": {
    "command": "uv",
    "args": ["--directory", "D:/Dev/repos/calibre-mcp", "run", "calibre-mcp"]
  }
}

1. PyPI Package Install (Recommended) ⭐

Simple pip installation - no repository cloning required!

# Install from PyPI
pip install calibre-mcp

# Configure Claude Desktop
{
  "mcpServers": {
    "calibre-mcp": {
      "command": "python",
      "args": ["-m", "calibre_mcp.server"],
      "env": {
        "CALIBRE_LIBRARY_PATH": "C:/path/to/your/calibre/library"
      }
    }
  }
}

Advantages:

• ✅ Universal compatibility - Works with any MCP client
• ✅ Simple installation - Just one pip command
• ✅ Always up-to-date - Install latest version directly
• ✅ No repository cloning - Clean, minimal setup
• ✅ Easy updates - pip install --upgrade calibre-mcp

---

2. MCPB Package ⭐

One-click installation via MCPB package - no Python setup required!

⚠️ Important: MCPB packages only work with Claude Desktop. They do not work with other MCP clients. The installation method is drag-and-drop only - there is no command-line installation option.

What is MCPB?

MCPB (Model Context Protocol Bundle) is a packaging format for MCP servers that bundles everything needed into a single .mcpb file. It's Claude Desktop's preferred installation method.

Install MCPB CLI (for building packages)

The MCPB CLI is only needed if you want to build packages from source. End users don't need it.

# Install MCPB CLI globally
npm install -g @anthropic-ai/mcpb

# Or use npx (no installation needed)
npx @anthropic-ai/mcpb --version

Get the CalibreMCP Package

Option A: Download Pre-built Package

• Download the latest calibre-mcp.mcpb from Releases

Option B: Build from Source (requires cloning repo)

# Install MCPB CLI
npm install -g @anthropic-ai/mcpb

# Clone repository
git clone https://github.com/sandra/calibre-mcp.git
cd calibre-mcp

# Build the package
.\scripts\build-mcpb-package.ps1

# Package will be created at: dist\calibre-mcp.mcpb

Install in Claude Desktop (Drag-and-Drop Only)

⚠️ Important: MCPB packages must be installed via drag-and-drop in Claude Desktop. There is no command-line installation method.

1. Open Claude Desktop Settings:

   • Click the ⚙️ Settings icon in Claude Desktop
   • Navigate to the MCP Servers or Extensions section

2. Drag and Drop the Package:

   • Locate the calibre-mcp.mcpb file on your computer
   • Drag the file into the Claude Desktop settings page
   • Drop it in the designated area (usually shows "Drop MCPB package here" or similar)

3. Wait for Installation:

   • Claude Desktop will automatically:
     • Extract the package
     • Install dependencies
     • Register the server
   • You'll see a success message when complete

4. Configure:

   • After installation, Claude Desktop will prompt you to configure:
     • Calibre Library Path (required) - Path to your Calibre library directory
     • Calibre Server URL (optional) - URL if using Calibre Content Server
     • Timeout (optional) - Operation timeout in seconds

5. Verify Installation:

   • The server should appear in your MCP servers list
   • You should see "20 tools, 20 prompts" in the Claude Desktop interface
   • Test by asking Claude to list your libraries or books

✅ Benefits:

• No Python environment setup required
• Automatic dependency management
• One-click (drag-and-drop) installation
• Works across all platforms (Windows, macOS, Linux)
• All dependencies bundled in the package

❌ Limitations:

• Only works with Claude Desktop (not other MCP clients)
• Must use drag-and-drop (no CLI installation)
• Requires Claude Desktop to be running during installation

---

3. Standard MCP Configuration (Manual Setup)

For Claude Desktop and other MCP clients, add to your claude_desktop_config.json:

{
  "mcpServers": {
    "calibre-mcp": {
      "command": "python",
      "args": ["src/calibre_mcp/server.py"],
      "cwd": "/path/to/calibre-mcp",
      "env": {
        "PYTHONPATH": "src",
        "CALIBRE_SERVER_URL": "http://localhost:8080",
        "PYTHONUNBUFFERED": "1"
      }
    }
  }
}

For other MCP clients, configure according to your client's MCP server setup requirements.

✅ Benefits:

• No repository cloning required
• Simple pip install
• Automatic dependency management
• Works with any MCP client

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3. Python Development Install (Legacy/Development)

⚠️ Note: This method requires cloning the repository and setting up a Python environment.

🚀 Installation

Prerequisites

• uv installed (RECOMMENDED)
• Python 3.12+

📦 Quick Start

Run immediately via uvx:

uvx calibre-mcp

🎯 Claude Desktop Integration

Add to your claude_desktop_config.json:

"mcpServers": {
  "calibre-mcp": {
    "command": "uv",
    "args": ["--directory", "D:/Dev/repos/calibre-mcp", "run", "calibre-mcp"]
  }
}

Test Connection

python -c "
from calibre_mcp.calibre_api import quick_library_test
import asyncio
print('✅ Working' if asyncio.run(quick_library_test()) else '❌ Failed')
"

Verify Server Will Load in Claude ⚡

Before committing or restarting Claude, verify the server will load:

# Quick check (fast, recommended)
python scripts/quick_check.py

# Comprehensive check (includes linting)
python scripts/pre_commit_check.py

# Check recent logs for errors
python scripts/check_logs.py --errors-only

Standardized Workflow (After "add tool to do x"):

1. Add tool following all standards
2. Run ruff iteratively until zero errors:
   • uv run ruff check . → Fix errors → Run again → Repeat until clean
   • uv run ruff format . (format after linting passes)
3. Run pytest: uv run python -m pytest -v
4. Run restart script: .\scripts\restart_claude_and_check_mcp.ps1

Quick Check (Claude Already Running):

.\scripts\restart_claude_and_check_mcp.ps1 -NoRestart

Full Restart & Check:

.\scripts\restart_claude_and_check_mcp.ps1

Note:

• SOTA Script - Generic MCP server debugging script (copied from central docs repo)
• Uses taskkill to stop Claude (no UAC needed)
• Auto-detects server name, log file location, Claude path
• Checks only the LAST startup attempt (ignores older failures)
• See MCP Development Workflow for complete process

Features

Core Features

• Advanced Search - Powerful filtering and search capabilities:
  • Text search across titles, authors, series, tags, and comments
  • Filter by publication date and date added
  • File size and format filtering
  • Empty/non-empty comments
  • Star ratings (exact, minimum, or unrated)
  • Publisher filtering (single or multiple)
• Full Library Access - Browse and manage your entire Calibre collection
• Smart Metadata - Automatic metadata enhancement and validation
• Format Conversion - On-the-fly conversion between formats
• Reading Progress - Track reading progress across devices
• Tag Management - Smart tagging and categorization
• Library Statistics - Detailed insights into your collection
• Advanced OCR Processing - Multiple OCR backends with different capabilities:
  • GOT-OCR2.0 Integration: Revolutionary open-source OCR with formatted text preservation
  • ABBYY FineReader CLI: Traditional commercial OCR with multi-language support
  • OCR Modes: Plain text, formatted text, fine-grained region OCR, HTML rendering
  • Auto Provider Selection: Automatically chooses best available OCR backend

AI-Powered Tools

• Local LLM Auto-Discovery - Automatically detects and connects to running Ollama/LM Studio instances (no config needed)
• Book Recommendations - Get personalized book suggestions based on content similarity
• Content Analysis - Extract entities, themes, and sentiment from book content
• Reading Insights - Analyze your reading habits and preferences
• Metadata Enhancement - AI-assisted metadata correction and enrichment

Advanced Series Management

• Series Analysis - Identify and fix issues in book series
• Metadata Validation - Ensure consistency across your library
• Series Merging - Combine duplicate or related series
• Automatic Organization - Keep your series properly ordered and managed

🤖 AI Hardware Requirements

While basic operations run on any hardware, AI-powered features (Ollama integration, local embeddings) have specific requirements:

• Minimal (No AI): Any CPU, 4GB RAM. Works for search, metadata, and webapp browsing.
• Recommended (Local LLM 8B): NVIDIA RTX 3060 (12GB VRAM) or Mac M1/M2/M3 (16GB+). Capable of chat and basic analysis.
• High Performance (Local LLM 70B): NVIDIA RTX 3090/4090 (24GB VRAM) or Mac Studio. Required for deep content analysis and complex reasoning.

[!IMPORTANT] Local LLM Usage: AI features are OPTIONAL. You can run CalibreMCP without any GPU. If enabling local AI (Ollama), ensure your hardware meets the model's requirements. CPU inference is available but significantly slower.

🌟 Natural Language Search & Auto-Open

CalibreMCP supports conversational book access with intelligent parsing and automatic viewer launching.

Natural Language Search

The MCP client LLM automatically parses natural language queries into structured searches:

User Input → MCP Tool Call:

• "search books by harari" → query_books(operation="search", author="harari")
• "books about china" → query_books(operation="search", tag="china")
• "books from last year" → query_books(operation="search", added_after="2024-01-01")
• "find sapiens" → query_books(operation="search", title="sapiens")

FastMCP 2.14.4 Sampling (SEP-1577): For ambiguous queries, the server uses ctx.sample() with tools to orchestrate agentic workflows. Supports dialogic returns (execution_time_ms, recommendations) for SOTA MCP compliance.

Auto-Open Books

When searches return exactly 1 result, books automatically launch in your system's default viewer:

# Auto-open enabled
result = query_books(operation="search", title="homo deus", auto_open=True)

# Returns:
{
  "total": 1,
  "auto_opened": true,
  "opened_book": {
    "title": "Homo Deus: A Brief History of Tomorrow",
    "format": "EPUB"
  }
}

Supported Formats: EPUB, PDF, MOBI, AZW3, CBZ, CBR, TXT, HTML, RTF

Book Access Commands

Natural Phrasings:

• "I want to read 'Homo Deus'" → Search & auto-open
• "Show me 'The Burning Court'" → Search & auto-open
• "Open 'Sapiens' for reading" → Search & auto-open

All map to: Title search with auto-open enabled

Usage Examples

Natural Language & Auto-Open Examples

Conversational Book Access: The MCP client parses natural language and auto-opens books:

# User says: "I want to read 'Homo Deus'" → Auto-search and open
results = await query_books(
    operation="search",
    title="homo deus",
    auto_open=True,           # Auto-launch viewer if unique result
    auto_open_format="EPUB"   # Preferred format
)
# Result: Book opens in system viewer, returns metadata

# User says: "books about china" → Tag search
results = await query_books(
    operation="search",
    tag="china"
)

# User says: "find sapiens" → Title search with sampling
results = await query_books(
    operation="search",
    title="sapiens"  # Fast title-only search (bypasses FTS)
)

Search Examples

Using Portmanteau Tools: All user queries (search, list, find, query, get) map to query_books:

# User says: "list books by conan doyle" → query_books with operation="search"
results = await query_books(
    operation="search",
    author="Conan Doyle",
    format_table=True
)

# Search by title, author, or series
results = await query_books(
    operation="search",
    text="python programming"
)

# Filter by date ranges
from datetime import datetime, timedelta
last_month = (datetime.now() - timedelta(days=30)).strftime("%Y-%m-%d")
today = datetime.now().strftime("%Y-%m-%d")
results = await query_books(
    operation="search",
    pubdate_start="2020-01-01",
    added_after=last_month,
    added_before=today
)

# Filter by file size and format
results = await query_books(
    operation="search",
    min_size=1048576,    # 1MB
    max_size=10485760,   # 10MB
    formats=["EPUB", "PDF"]
)

# Find books with empty comments or no publisher
results = await query_books(
    operation="search",
    has_empty_comments=True,
    has_publisher=False
)

# Find highly rated books
results = await query_books(operation="search", min_rating=4)

# Find unrated books
results = await query_books(operation="search", unrated=True)

# Find books by publisher
results = await query_books(operation="search", publisher="O'Reilly Media")

# Combined search with multiple filters
results = await query_books(
    operation="search",
    text="machine learning",
    min_rating=4,
    pubdate_start="2020-01-01",
    formats=["EPUB"],
    limit=20,
    offset=0
)

# List all books (simple operation, no filters)
results = await query_books(operation="list", limit=50)

OCR Processing Examples

# Auto-select best OCR provider (GOT-OCR2.0 preferred, FineReader fallback)
result = await process_ocr(source="/path/to/scanned_book.pdf")

# Use GOT-OCR2.0 for formatted text preservation
result = await process_ocr(
    source="/path/to/book_page.png",
    provider="got-ocr",
    ocr_mode="format",
    render_html=True
)

# Traditional FineReader OCR for searchable PDFs
result = await process_ocr(
    source="/path/to/scanned_document.pdf",
    provider="finereader",
    language="english",
    output_format="pdf"
)

# Fine-grained OCR on specific region
result = await process_ocr(
    source="/path/to/document.png",
    provider="got-ocr",
    ocr_mode="fine-grained",
    region=[100, 200, 400, 500]  # [x1, y1, x2, y2]
)

AI-Powered Recommendations

# Get book recommendations based on a book
recommendations = await get_book_recommendations(book_id="123")

# Get personalized recommendations based on preferences
preferences = {
    "favorite_authors": ["Brandon Sanderson", "N.K. Jemisin"],
    "favorite_genres": ["Fantasy", "Science Fiction"]
}
personalized_recs = await get_book_recommendations(user_preferences=preferences)

Content Analysis

# Analyze book content
analysis = await analyze_book_content(
    book_content="...",
    options={
        "extract_entities": True,
        "analyze_sentiment": True,
        "identify_themes": True
    }
)

Series Management

# Analyze series in your library
analysis = await analyze_series(
    library_path="/path/to/calibre/library",
    update_metadata=True
)

# Fix common series metadata issues
fix_results = await fix_series_metadata(
    library_path="/path/to/calibre/library",
    dry_run=False
)

# Merge two series
merge_results = await merge_series(
    library_path="/path/to/calibre/library",
    source_series="The Kingkiller Chronicle",
    target_series="Kingkiller Chronicle",
    dry_run=True
)

Documentation

Full documentation available at docs/.

Testing

# Unit tests
python -m pytest tests/test_api.py -v

# Integration tests  
python -m pytest tests/integration_tests.py -v

# MCP Inspector
python -m calibre_mcp.server
# Opens: http://127.0.0.1:6274

🔧 Configuration

Environment Variables

CALIBRE_SERVER_URL=http://localhost:8080
CALIBRE_USERNAME=your_username      # If auth enabled
CALIBRE_PASSWORD=your_password      # If auth enabled
CALIBRE_TIMEOUT=30
CALIBRE_DEFAULT_LIMIT=50

With Authentication

# Enable Calibre authentication
calibre-server --port=8080 --enable-auth --manage-users

# Add user and set environment
export CALIBRE_USERNAME="sandra"
export CALIBRE_PASSWORD="your_password"

🚨 Troubleshooting

Connection Failed

# Check if Calibre server is running
netstat -an | findstr 8080  # Windows
curl http://localhost:8080  # Test in browser

No Books Found

# Verify library has books
calibredb list --limit=5

# Check library path
calibre-debug --get-library

Authentication Issues

# Test credentials
curl -u username:password http://localhost:8080/ajax/interface-data/init

See Troubleshooting Guide for comprehensive solutions.

🏗 Architecture

calibremcp/
├── src/calibre_mcp/
│   ├── server.py           # 4 FastMCP tools
│   ├── calibre_api.py      # HTTP API client
│   └── config.py           # Configuration management
├── config/
│   ├── settings.yaml       # Server configuration
│   └── calibre_config.yaml # Calibre-specific settings
├── tests/
│   ├── test_api.py         # Unit tests
│   └── integration_tests.py # Integration tests
└── docs/                   # Complete documentation

💯 Success Metrics

Phase 1 Complete ✅

• ✅ 4/4 core MCP tools implemented and tested
• ✅ FastMCP 2.0 compliance with proper structure
• ✅ Comprehensive error handling and diagnostics
• ✅ Austrian efficiency: 45-minute realistic timeline
• ✅ Production-ready for immediate use with Claude Desktop and other MCP clients

📄 License

MIT License - Austrian efficiency in e-book management.

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Built with Austrian efficiency for Sandra's comprehensive e-book workflow.

"Sin temor y sin esperanza" - practical without hype or doom.