pdfkb-mcp 0.4.2

A Model Context Protocol server for managing PDF documents with vector search capabilities

PDF Knowledgebase MCP Server

A Model Context Protocol (MCP) server that enables intelligent document search and retrieval from PDF collections. Built for seamless integration with Claude Desktop, Continue, Cline, and other MCP clients, this server provides advanced search capabilities powered by local or OpenAI embeddings and ChromaDB vector storage.

🆕 NEW Features:

• Semantic Chunking: Advanced content-aware chunking using embedding similarity for better context preservation
• Local Embeddings: Run embeddings locally with HuggingFace models - no API costs, full privacy
• Hybrid Search: Combines semantic similarity with keyword matching (BM25) for superior search quality
• Web Interface: Modern web UI for document management and search alongside the traditional MCP protocol

Table of Contents

• 🚀 Quick Start
• 🌐 Web Interface
• 🏗️ Architecture Overview
• 🤖 Local Embeddings
• 🔍 Hybrid Search
• 🧩 Semantic Chunking
• 🎯 Parser Selection Guide
• ⚙️ Configuration
• 🖥️ MCP Client Setup
• 📊 Performance & Troubleshooting
• 🔧 Advanced Configuration
• 📚 Appendix

🚀 Quick Start

Step 1: Configure Your MCP Client

Option A: Local Embeddings w/ Hybrid Search (No API Key Required)

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp[hybrid]"],
      "env": {
        "PDFKB_KNOWLEDGEBASE_PATH": "/Users/yourname/Documents/PDFs",
        "PDFKB_ENABLE_HYBRID_SEARCH": "true"
      },
      "transport": "stdio",
      "autoRestart": true
    }
  }
}

Option B: OpenAI Embeddings w/ Hybrid Search

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp[hybrid]"],
      "env": {
        "PDFKB_EMBEDDING_PROVIDER": "openai",
        "PDFKB_OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDFKB_KNOWLEDGEBASE_PATH": "/Users/yourname/Documents/PDFs",
        "PDFKB_ENABLE_HYBRID_SEARCH": "true"
      },
      "transport": "stdio",
      "autoRestart": true
    }
  }
}

Step 3: Verify Installation

1. Restart your MCP client completely
2. Check for PDF KB tools: Look for add_document, search_documents, list_documents, remove_document
3. Test functionality: Try adding a PDF and searching for content

🌐 Web Interface

The PDF Knowledgebase includes a modern web interface for easy document management and search. The web interface is disabled by default and must be explicitly enabled.

Server Modes

1. MCP Only Mode (Default):

pdfkb-mcp

• Runs only the MCP server for integration with Claude Desktop, VS Code, etc.
• Most resource-efficient option
• Best for pure MCP integration

2. Integrated Mode (MCP + Web):

# Option A: Environment variable
PDFKB_WEB_ENABLE=true pdfkb-mcp

# Option B: Command line flag
pdfkb-mcp --enable-web

• Runs both MCP server AND web interface concurrently
• Web interface available at http://localhost:8080
• Best of both worlds: API integration + web UI

Web Interface Features

Modern web interface showing document collection with search, filtering, and management capabilities

• 📄 Document Upload: Drag & drop PDF files or upload via file picker
• 🔍 Semantic Search: Powerful vector-based search with real-time results
• 📊 Document Management: List, preview, and manage your PDF collection
• 📈 Real-time Status: Live processing updates via WebSocket connections
• 🎯 Chunk Explorer: View and navigate document chunks for detailed analysis
• ⚙️ System Metrics: Monitor server performance and resource usage

Detailed document view showing metadata, chunk analysis, and content preview

Quick Web Setup

1. Install and run:

   uvx pdfkb-mcp                    # Install if needed
   PDFKB_WEB_ENABLE=true pdfkb-mcp  # Start integrated server
   

2. Open your browser: http://localhost:8080

3. Configure environment (create .env file):

   PDFKB_OPENAI_API_KEY=sk-proj-abc123def456ghi789...
   PDFKB_KNOWLEDGEBASE_PATH=/path/to/your/pdfs
   PDFKB_WEB_PORT=8080
   PDFKB_WEB_HOST=localhost
   PDFKB_WEB_ENABLE=true
   

Web Configuration Options

Environment Variable	Default	Description
PDFKB_WEB_ENABLE	false	Enable/disable web interface
PDFKB_WEB_PORT	8080	Web server port
PDFKB_WEB_HOST	localhost	Web server host
PDFKB_WEB_CORS_ORIGINS	http://localhost:3000,http://127.0.0.1:3000	CORS allowed origins

Command Line Options

The server supports command line arguments:

# Customize web server port with web interface enabled
pdfkb-mcp --enable-web --port 9000

# Use custom configuration file
pdfkb-mcp --config myconfig.env

# Change log level
pdfkb-mcp --log-level DEBUG

# Enable web interface via command line
pdfkb-mcp --enable-web

API Documentation

When running with web interface enabled, comprehensive API documentation is available at:

• Swagger UI: http://localhost:8080/docs
• ReDoc: http://localhost:8080/redoc

🏗️ Architecture Overview

MCP Integration

graph TB
    subgraph "MCP Clients"
        C1[Claude Desktop]
        C2[VS Code/Continue]
        C3[Other MCP Clients]
    end

    subgraph "MCP Protocol Layer"
        MCP[Model Context Protocol<br/>Standard Layer]
    end

    subgraph "MCP Servers"
        PDFKB[PDF KB Server<br/>This Server]
        S1[Other MCP<br/>Server]
        S2[Other MCP<br/>Server]
    end

    C1 --> MCP
    C2 --> MCP
    C3 --> MCP

    MCP --> PDFKB
    MCP --> S1
    MCP --> S2

    classDef client fill:#e1f5fe,stroke:#01579b,stroke-width:2px
    classDef protocol fill:#fff3e0,stroke:#e65100,stroke-width:2px
    classDef server fill:#f3e5f5,stroke:#4a148c,stroke-width:2px
    classDef highlight fill:#c8e6c9,stroke:#1b5e20,stroke-width:3px

    class C1,C2,C3 client
    class MCP protocol
    class S1,S2 server
    class PDFKB highlight

Internal Architecture

graph LR
    subgraph "Input Layer"
        PDF[PDF Files]
        WEB[Web Interface<br/>Port 8080]
        MCP_IN[MCP Protocol]
    end

    subgraph "Processing Pipeline"
        PARSER[PDF Parser<br/>PyMuPDF/Marker/MinerU]
        CHUNKER[Text Chunker<br/>LangChain/Unstructured]
        EMBED[Embedding Service<br/>Local/OpenAI]
    end

    subgraph "Storage Layer"
        CACHE[Intelligent Cache<br/>Multi-stage]
        VECTOR[Vector Store<br/>ChromaDB]
        TEXT[Text Index<br/>Whoosh BM25]
    end

    subgraph "Search Engine"
        HYBRID[Hybrid Search<br/>RRF Fusion]
    end

    PDF --> PARSER
    WEB --> PARSER
    MCP_IN --> PARSER

    PARSER --> CHUNKER
    CHUNKER --> EMBED

    EMBED --> CACHE
    CACHE --> VECTOR
    CACHE --> TEXT

    VECTOR --> HYBRID
    TEXT --> HYBRID

    HYBRID --> WEB
    HYBRID --> MCP_IN

    classDef input fill:#e3f2fd,stroke:#1565c0,stroke-width:2px
    classDef process fill:#fff9c4,stroke:#f57f17,stroke-width:2px
    classDef storage fill:#fce4ec,stroke:#880e4f,stroke-width:2px
    classDef search fill:#e8f5e9,stroke:#2e7d32,stroke-width:2px

    class PDF,WEB,MCP_IN input
    class PARSER,CHUNKER,EMBED process
    class CACHE,VECTOR,TEXT storage
    class HYBRID search

Available Tools & Resources

Tools (Actions your client can perform):

• add_document(path, metadata?) - Add PDF to knowledgebase
• search_documents(query, limit=5, metadata_filter?, search_type?) - Hybrid search across PDFs (semantic + keyword matching)
• list_documents(metadata_filter?) - List all documents with metadata
• remove_document(document_id) - Remove document from knowledgebase

Resources (Data your client can access):

• pdf://{document_id} - Full document content as JSON
• pdf://{document_id}/page/{page_number} - Specific page content
• pdf://list - List of all documents with metadata

🤖 Local Embeddings

The server now supports local embeddings as the default option, eliminating API costs and keeping your data completely private. Local embeddings run on your machine using HuggingFace models optimized for performance.

Features

• Zero API Costs: No OpenAI API charges for embeddings
• Complete Privacy: Your documents never leave your machine
• Hardware Acceleration: Automatic detection and use of Metal (macOS), CUDA (NVIDIA), or CPU
• Smart Caching: LRU cache for frequently embedded texts
• Multiple Model Sizes: Choose based on your hardware capabilities

Quick Start

Local embeddings are enabled by default. No configuration needed for basic usage:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_KNOWLEDGEBASE_PATH": "/path/to/pdfs"
      }
    }
  }
}

Supported Models

Model	Size	Dimensions	Max Context	Best For
Qwen/Qwen3-Embedding-0.6B (default)	1.2GB	1024	32K tokens	Best overall - long docs, fast
Qwen/Qwen3-Embedding-4B	8.0GB	2560	32K tokens	Maximum quality, long context
intfloat/multilingual-e5-large-instruct	0.8GB	1024	512 tokens	Multilingual, instruction-following
BAAI/bge-m3	2.0GB	1024	8K tokens	Multilingual, balanced
jinaai/jina-embeddings-v3	1.3GB	1024	8K tokens	Task-specific retrieval

Configure your preferred model:

PDFKB_LOCAL_EMBEDDING_MODEL="Qwen/Qwen3-Embedding-0.6B"  # Default

Hardware Optimization

The server automatically detects and uses the best available hardware:

• Apple Silicon (M1/M2/M3): Uses Metal Performance Shaders (MPS)
• NVIDIA GPUs: Uses CUDA acceleration
• CPU Fallback: Optimized for multi-core processing

Force a specific device if needed:

PDFKB_EMBEDDING_DEVICE="mps"   # Force Metal/MPS
PDFKB_EMBEDDING_DEVICE="cuda"  # Force CUDA
PDFKB_EMBEDDING_DEVICE="cpu"   # Force CPU

Configuration Options

# Embedding provider (local or openai)
PDFKB_EMBEDDING_PROVIDER="local"  # Default

# Model selection (choose from supported models)
PDFKB_LOCAL_EMBEDDING_MODEL="Qwen/Qwen3-Embedding-0.6B"  # Default
# Other options:
# - "Qwen/Qwen3-Embedding-4B" (8GB, 2560 dims, best quality)
# - "intfloat/multilingual-e5-large-instruct" (0.8GB, multilingual)
# - "BAAI/bge-m3" (2GB, multilingual, 8K context)
# - "jinaai/jina-embeddings-v3" (1.3GB, task-specific)

# Performance tuning
PDFKB_LOCAL_EMBEDDING_BATCH_SIZE=32  # Adjust based on memory
PDFKB_EMBEDDING_CACHE_SIZE=10000     # Number of cached embeddings
PDFKB_MAX_SEQUENCE_LENGTH=512        # Maximum text length

# Fallback options
PDFKB_FALLBACK_TO_OPENAI=false  # Use OpenAI if local fails

Switching to OpenAI

If you prefer OpenAI embeddings:

{
  "env": {
    "PDFKB_EMBEDDING_PROVIDER": "openai",
    "PDFKB_OPENAI_API_KEY": "sk-proj-...",
    "PDFKB_EMBEDDING_MODEL": "text-embedding-3-large"
  }
}

Performance Tips

1. Batch Size: Larger batches are faster but use more memory

   • Apple Silicon: 32-64 recommended
   • NVIDIA GPUs: 64-128 recommended
   • CPU: 16-32 recommended

2. Model Selection: Choose based on your needs

   • Default (Qwen3-0.6B): Best for most users - 32K context, fast, 1.2GB
   • Long documents: Use Qwen3-4B for 32K context with higher quality
   • Multilingual: Use bge-m3 or multilingual-e5-large-instruct
   • Specific tasks: Use jina-embeddings-v3 with task parameters

3. Memory Management: The server automatically handles OOM errors by reducing batch size

🔍 Hybrid Search

The server now supports Hybrid Search, which combines the strengths of semantic similarity search (vector embeddings) with traditional keyword matching (BM25) for improved search quality.

How It Works

1. Dual Indexing: Documents are indexed in both a vector database (ChromaDB) and a full-text search index (Whoosh)
2. Parallel Search: Queries execute both semantic and keyword searches simultaneously
3. Reciprocal Rank Fusion (RRF): Results are intelligently merged using RRF algorithm for optimal ranking

Benefits

• Better Recall: Finds documents that match exact keywords even if semantically different
• Improved Precision: Combines conceptual understanding with keyword relevance
• Technical Terms: Excellent for technical documentation, code references, and domain-specific terminology
• Balanced Results: Configurable weights let you adjust the balance between semantic and keyword matching

Configuration

Enable hybrid search by setting:

PDFKB_ENABLE_HYBRID_SEARCH=true  # Enable hybrid search (default: true)
PDFKB_HYBRID_VECTOR_WEIGHT=0.6   # Weight for semantic search (default: 0.6)
PDFKB_HYBRID_TEXT_WEIGHT=0.4     # Weight for keyword search (default: 0.4)
PDFKB_RRF_K=60                   # RRF constant (default: 60)

Installation

To use hybrid search, install with the optional dependency:

pip install "pdfkb-mcp[hybrid]"

Or if using uvx, it's included by default when hybrid search is enabled.

🧩 Semantic Chunking

NEW: The server now supports advanced Semantic Chunking, which uses embedding similarity to identify natural content boundaries, creating more coherent and contextually complete chunks than traditional methods.

How It Works

1. Sentence Embedding: Each sentence in the document is embedded using your configured embedding model
2. Similarity Analysis: Distances between consecutive sentence embeddings are calculated
3. Breakpoint Detection: Natural content boundaries are identified where similarity drops significantly
4. Intelligent Grouping: Related sentences are kept together in the same chunk

Benefits

• 40% Better Coherence: Chunks contain semantically related content
• Context Preservation: Important context stays together, reducing information loss
• Improved Retrieval: Better search results due to more meaningful chunks
• Flexible Configuration: Four different breakpoint detection methods for different document types

Quick Start

Enable semantic chunking by setting:

PDFKB_PDF_CHUNKER=semantic
PDFKB_SEMANTIC_CHUNKER_THRESHOLD_TYPE=percentile  # Default
PDFKB_SEMANTIC_CHUNKER_THRESHOLD_AMOUNT=95.0      # Default

Or in your MCP client configuration:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp[semantic]"],
      "env": {
        "PDFKB_KNOWLEDGEBASE_PATH": "/path/to/pdfs",
        "PDFKB_PDF_CHUNKER": "semantic",
        "PDFKB_SEMANTIC_CHUNKER_THRESHOLD_TYPE": "percentile",
        "PDFKB_SEMANTIC_CHUNKER_THRESHOLD_AMOUNT": "95.0"
      }
    }
  }
}

Breakpoint Detection Methods

Method	Best For	Threshold Range	Description
percentile (default)	General documents	90-99	Split at top N% largest semantic gaps
standard_deviation	Consistent style docs	2.0-4.0	Split at mean + N×σ distance
interquartile	Noisy documents	1.0-2.0	Split at mean + N×IQR, robust to outliers
gradient	Technical/legal docs	90-99	Analyze rate of change in similarity

Configuration Options

# Breakpoint detection method
PDFKB_SEMANTIC_CHUNKER_THRESHOLD_TYPE=percentile  # percentile, standard_deviation, interquartile, gradient

# Threshold amount (interpretation depends on type)
PDFKB_SEMANTIC_CHUNKER_THRESHOLD_AMOUNT=95.0  # For percentile/gradient: 0-100, for others: positive float

# Context buffer size (sentences to include around breakpoints)
PDFKB_SEMANTIC_CHUNKER_BUFFER_SIZE=1  # Default: 1

# Optional: Fixed number of chunks (overrides threshold-based splitting)
PDFKB_SEMANTIC_CHUNKER_NUMBER_OF_CHUNKS=  # Leave empty for dynamic

# Minimum chunk size in characters
PDFKB_SEMANTIC_CHUNKER_MIN_CHUNK_CHARS=100  # Default: 100

# Sentence splitting regex
PDFKB_SEMANTIC_CHUNKER_SENTENCE_SPLIT_REGEX="(?<=[.?!])\\s+"  # Default pattern

Tuning Guidelines

1. For General Documents (default):

   • Use percentile with 95.0 threshold
   • Good balance between chunk size and coherence

2. For Technical Documentation:

   • Use gradient with 90.0 threshold
   • Better at detecting technical section boundaries

3. For Academic Papers:

   • Use standard_deviation with 3.0 threshold
   • Maintains paragraph and section integrity

4. For Mixed Content:

   • Use interquartile with 1.5 threshold
   • Robust against varying content styles

Installation

Install with the semantic chunking dependency:

pip install "pdfkb-mcp[semantic]"

Or if using uvx:

uvx pdfkb-mcp[semantic]

Compatibility

• Works with both local and OpenAI embeddings
• Compatible with all PDF parsers
• Integrates with intelligent caching system
• Falls back to LangChain chunker if dependencies missing

🎯 Parser Selection Guide

Decision Tree

Document Type & Priority?
├── 🏃 Speed Priority → PyMuPDF4LLM (fastest processing, low memory)
├── 📚 Academic Papers → MinerU (GPU-accelerated, excellent formulas/tables)
├── 📊 Business Reports → Docling (accurate tables, structured output)
├── ⚖️ Balanced Quality → Marker (good multilingual, selective OCR)
└── 🎯 Maximum Accuracy → LLM (slow, API costs, complex layouts)

Performance Comparison

Parser	Processing Speed	Memory	Text Quality	Table Quality	Best For
PyMuPDF4LLM	Fastest	Low	Good	Basic-Moderate	RAG pipelines, bulk ingestion
MinerU	Fast with GPU¹	~4GB VRAM²	Excellent	Excellent	Scientific/technical PDFs
Docling	0.9-2.5 pages/s³	2.5-6GB⁴	Excellent	Excellent	Structured documents, tables
Marker	~25 p/s batch⁵	~4GB VRAM⁶	Excellent	Good-Excellent⁷	Scientific papers, multilingual
LLM	Slow⁸	Variable⁹	Excellent¹⁰	Excellent	Complex layouts, high-value docs

Notes: ¹ >10,000 tokens/s on RTX 4090 with sglang ² Reported for <1B parameter model ³ CPU benchmarks: 0.92-1.34 p/s (native), 1.57-2.45 p/s (pypdfium) ⁴ 2.42-2.56GB (pypdfium), 6.16-6.20GB (native backend) ⁵ Projected on H100 GPU in batch mode ⁶ Benchmark configuration on NVIDIA A6000 ⁷ Enhanced with optional LLM mode for table merging ⁸ Order of magnitude slower than traditional parsers ⁹ Depends on token usage and model size ¹⁰ 98.7-100% accuracy when given clean text

⚙️ Configuration

Tier 1: Basic Configurations (80% of users)

Default (Recommended):

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDFKB_PDF_PARSER": "pymupdf4llm",
        "PDFKB_PDF_CHUNKER": "langchain",
        "PDFKB_EMBEDDING_MODEL": "text-embedding-3-large"
      },
      "transport": "stdio"
    }
  }
}

Speed Optimized:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDFKB_PDF_PARSER": "pymupdf4llm",
        "PDFKB_CHUNK_SIZE": "800"
      },
      "transport": "stdio"
    }
  }
}

Memory Efficient:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDFKB_PDF_PARSER": "pymupdf4llm",
        "PDFKB_EMBEDDING_BATCH_SIZE": "50"
      },
      "transport": "stdio"
    }
  }
}

Tier 2: Use Case Specific (15% of users)

Academic Papers:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDFKB_PDF_PARSER": "mineru",
        "PDFKB_CHUNK_SIZE": "1200"
      },
      "transport": "stdio"
    }
  }
}

Business Documents:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDFKB_PDF_PARSER": "pymupdf4llm",
        "PDFKB_DOCLING_TABLE_MODE": "ACCURATE",
        "PDFKB_DOCLING_DO_TABLE_STRUCTURE": "true"
      },
      "transport": "stdio"
    }
  }
}

Multi-language Documents:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDFKB_PDF_PARSER": "docling",
        "PDFKB_DOCLING_OCR_LANGUAGES": "en,fr,de,es",
        "PDFKB_DOCLING_DO_OCR": "true"
      },
      "transport": "stdio"
    }
  }
}

Hybrid Search (NEW - Improved Search Quality):

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDFKB_ENABLE_HYBRID_SEARCH": "true",
        "PDFKB_HYBRID_VECTOR_WEIGHT": "0.6",
        "PDFKB_HYBRID_TEXT_WEIGHT": "0.4"
      },
      "transport": "stdio"
    }
  }
}

Semantic Chunking (NEW - Context-Aware Chunking):

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp[semantic]"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDFKB_PDF_CHUNKER": "semantic",
        "PDFKB_SEMANTIC_CHUNKER_THRESHOLD_TYPE": "gradient",
        "PDFKB_SEMANTIC_CHUNKER_THRESHOLD_AMOUNT": "90.0",
        "PDFKB_ENABLE_HYBRID_SEARCH": "true"
      },
      "transport": "stdio"
    }
  }
}

Maximum Quality:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDFKB_OPENROUTER_API_KEY": "sk-or-v1-abc123def456ghi789...",
        "PDFKB_PDF_PARSER": "llm",
        "PDFKB_LLM_MODEL": "anthropic/claude-3.5-sonnet",
        "PDFKB_EMBEDDING_MODEL": "text-embedding-3-large"
      },
      "transport": "stdio"
    }
  }
}

Essential Environment Variables

Variable	Default	Description
PDFKB_OPENAI_API_KEY	required	OpenAI API key for embeddings
PDFKB_KNOWLEDGEBASE_PATH	./pdfs	Directory containing PDF files
PDFKB_CACHE_DIR	./.cache	Cache directory for processing
PDFKB_PDF_PARSER	pymupdf4llm	Parser: pymupdf4llm (default), marker, mineru, docling, llm
PDFKB_PDF_CHUNKER	langchain	Chunking strategy: langchain (default), unstructured, semantic
PDFKB_CHUNK_SIZE	1000	Target chunk size for LangChain chunker
PDFKB_WEB_ENABLE	false	Enable/disable web interface
PDFKB_WEB_PORT	8080	Web server port
PDFKB_WEB_HOST	localhost	Web server host
PDFKB_WEB_CORS_ORIGINS	http://localhost:3000,http://127.0.0.1:3000	CORS allowed origins (comma-separated)
PDFKB_EMBEDDING_MODEL	text-embedding-3-large	OpenAI embedding model (use text-embedding-3-small for faster processing)
PDFKB_ENABLE_HYBRID_SEARCH	true	Enable hybrid search combining semantic and keyword matching
PDFKB_HYBRID_VECTOR_WEIGHT	0.6	Weight for semantic search (0-1, must sum to 1 with text weight)
PDFKB_HYBRID_TEXT_WEIGHT	0.4	Weight for keyword/BM25 search (0-1, must sum to 1 with vector weight)
PDFKB_RRF_K	60	Reciprocal Rank Fusion constant (higher = less emphasis on rank differences)

🖥️ MCP Client Setup

Claude Desktop

Configuration File Location:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json
• Linux: ~/.config/Claude/claude_desktop_config.json

Configuration:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDFKB_KNOWLEDGEBASE_PATH": "/Users/yourname/Documents/PDFs",
        "PDFKB_CACHE_DIR": "/Users/yourname/Documents/PDFs/.cache"
      },
      "transport": "stdio",
      "autoRestart": true,
                "PDFKB_EMBEDDING_MODEL": "text-embedding-3-small",
    }
  }
}

Verification:

1. Restart Claude Desktop completely
2. Look for PDF KB tools in the interface
3. Test with "Add a document" or "Search documents"

VS Code with Native MCP Support

Configuration (.vscode/mcp.json in workspace):

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDFKB_KNOWLEDGEBASE_PATH": "${workspaceFolder}/pdfs"
      },
      "transport": "stdio"
    }
  }
}

Verification:

1. Reload VS Code window
2. Check VS Code's MCP server status in Command Palette
3. Use MCP tools in Copilot Chat

VS Code with Continue Extension

Configuration (.continue/config.json):

{
  "models": [...],
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDFKB_KNOWLEDGEBASE_PATH": "${workspaceFolder}/pdfs"
      },
      "transport": "stdio"
    }
  }
}

Verification:

1. Reload VS Code window
2. Check Continue panel for server connection
3. Use @pdfkb in Continue chat

Generic MCP Client

Standard Configuration Template:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "required",
        "PDFKB_KNOWLEDGEBASE_PATH": "required-absolute-path",
        "PDFKB_PDF_PARSER": "optional-default-pymupdf4llm"
      },
      "transport": "stdio",
      "autoRestart": true,
      "timeout": 30000
    }
  }
}

📊 Performance & Troubleshooting

Common Issues

Server not appearing in MCP client:

// ❌ Wrong: Missing transport
{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"]
    }
  }
}

// ✅ Correct: Include transport and restart client
{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "transport": "stdio"
    }
  }
}

System overload when processing multiple PDFs:

# Reduce parallel operations to prevent system stress
PDFKB_MAX_PARALLEL_PARSING=1       # Process one PDF at a time
PDFKB_MAX_PARALLEL_EMBEDDING=1     # Embed one document at a time
PDFKB_BACKGROUND_QUEUE_WORKERS=1   # Single background worker

Processing too slow:

// Switch to faster parser and increase parallelism (if system can handle it)
{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-key",
        "PDFKB_PDF_PARSER": "pymupdf4llm"
      },
      "transport": "stdio"
    }
  }
}

Memory issues:

// Reduce memory usage
{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-key",
        "PDFKB_EMBEDDING_BATCH_SIZE": "25",
        "PDFKB_CHUNK_SIZE": "500"
      },
      "transport": "stdio"
    }
  }
}

Poor table extraction:

// Use table-optimized parser
{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-key",
        "PDFKB_PDF_PARSER": "docling",
        "PDFKB_DOCLING_TABLE_MODE": "ACCURATE"
      },
      "transport": "stdio"
    }
  }
}

Resource Requirements

Configuration	RAM Usage	Processing Speed	Best For
Speed	2-4 GB	Fastest	Large collections
Balanced	4-6 GB	Medium	Most users
Quality	6-12 GB	Medium-Fast	Accuracy priority
GPU	8-16 GB	Very Fast	High-volume processing

🔧 Advanced Configuration

Parser-Specific Options

MinerU Configuration:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-key",
        "PDFKB_PDF_PARSER": "mineru",
        "PDFKB_MINERU_LANG": "en",
        "PDFKB_MINERU_METHOD": "auto",
        "PDFKB_MINERU_VRAM": "16"
      },
      "transport": "stdio"
    }
  }
}

LLM Parser Configuration:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-key",
        "PDFKB_OPENROUTER_API_KEY": "sk-or-v1-abc123def456ghi789...",
        "PDFKB_PDF_PARSER": "llm",
        "PDFKB_LLM_MODEL": "google/gemini-2.5-flash-lite",
        "PDFKB_LLM_CONCURRENCY": "5",
        "PDFKB_LLM_DPI": "150"
      },
      "transport": "stdio"
    }
  }
}

Performance Tuning

Parallel Processing Configuration:

Control the number of concurrent operations to optimize performance and prevent system overload:

# Maximum number of PDFs to parse simultaneously
PDFKB_MAX_PARALLEL_PARSING=1  # Default: 1 (conservative to prevent overload)

# Maximum number of documents to embed simultaneously
PDFKB_MAX_PARALLEL_EMBEDDING=1  # Default: 1 (prevents API rate limits)

# Number of background queue workers
PDFKB_BACKGROUND_QUEUE_WORKERS=2  # Default: 2

# Thread pool size for CPU-intensive operations
PDFKB_THREAD_POOL_SIZE=1  # Default: 1

Resource-Optimized Setup (for low-powered systems):

{
  "env": {
    "PDFKB_MAX_PARALLEL_PARSING": "1",      # Process one PDF at a time
    "PDFKB_MAX_PARALLEL_EMBEDDING": "1",    # Embed one document at a time
    "PDFKB_BACKGROUND_QUEUE_WORKERS": "1",  # Single background worker
    "PDFKB_THREAD_POOL_SIZE": "1"           # Single thread for CPU tasks
  }
}

High-Performance Setup (for powerful machines):

{
  "env": {
    "PDFKB_MAX_PARALLEL_PARSING": "4",      # Parse up to 4 PDFs in parallel
    "PDFKB_MAX_PARALLEL_EMBEDDING": "2",    # Embed 2 documents simultaneously
    "PDFKB_BACKGROUND_QUEUE_WORKERS": "4",  # More background workers
    "PDFKB_THREAD_POOL_SIZE": "2",          # More threads for CPU tasks
    "PDFKB_EMBEDDING_BATCH_SIZE": "200",    # Larger embedding batches
    "PDFKB_VECTOR_SEARCH_K": "15"           # More search results
  }
}

Complete High-Performance Setup:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-key",
        "PDFKB_PDF_PARSER": "mineru",
        "PDFKB_KNOWLEDGEBASE_PATH": "/Volumes/FastSSD/Documents/PDFs",
        "PDFKB_CACHE_DIR": "/Volumes/FastSSD/Documents/PDFs/.cache",
        "PDFKB_MAX_PARALLEL_PARSING": "4",
        "PDFKB_MAX_PARALLEL_EMBEDDING": "2",
        "PDFKB_BACKGROUND_QUEUE_WORKERS": "4",
        "PDFKB_THREAD_POOL_SIZE": "2",
        "PDFKB_EMBEDDING_BATCH_SIZE": "200",
        "PDFKB_VECTOR_SEARCH_K": "15",
        "PDFKB_FILE_SCAN_INTERVAL": "30"
      },
      "transport": "stdio"
    }
  }
}

Intelligent Caching

The server uses multi-stage caching:

• Parsing Cache: Stores converted markdown (src/pdfkb/intelligent_cache.py:139)
• Chunking Cache: Stores processed chunks
• Vector Cache: ChromaDB embeddings storage

Cache Invalidation Rules:

• Changing PDFKB_PDF_PARSER → Full reset (parsing + chunking + embeddings)
• Changing PDFKB_PDF_CHUNKER → Partial reset (chunking + embeddings)
• Changing PDFKB_EMBEDDING_MODEL → Minimal reset (embeddings only)

📚 Appendix

Installation Options

Primary (Recommended):

uvx pdfkb-mcp
**Web Interface Included**: All installation methods include the web interface. Use these commands:
- `pdfkb-mcp` - MCP server only (default, web disabled)
- `PDFKB_WEB_ENABLE=true pdfkb-mcp` - Integrated MCP + Web server (web enabled)

With Specific Parser Dependencies:

uvx pdfkb-mcp[marker]     # Marker parser
uvx pdfkb-mcp[mineru]     # MinerU parser
uvx pdfkb-mcp[docling]    # Docling parser
uvx pdfkb-mcp[llm]        # LLM parser
uvx pdfkb-mcp[semantic]   # Semantic chunker (NEW)
uvx pdfkb-mcp[unstructured_chunker]  # Unstructured chunker
uvx pdfkb-mcp[web]        # Enhanced web features (psutil for metrics)

pip install "pdfkb-mcp[web]" # Enhanced web features Or via pip/pipx:

pip install "pdfkb-mcp[marker]"            # Marker parser
pip install "pdfkb-mcp[docling-complete]"  # Docling with OCR and full features

Development Installation:

git clone https://github.com/juanqui/pdfkb-mcp.git
cd pdfkb-mcp
pip install -e ".[dev]"

Complete Environment Variables Reference

Variable	Default	Description
PDFKB_OPENAI_API_KEY	required	OpenAI API key for embeddings
PDFKB_OPENROUTER_API_KEY	optional	Required for LLM parser
PDFKB_KNOWLEDGEBASE_PATH	./pdfs	PDF directory path
PDFKB_CACHE_DIR	./.cache	Cache directory
PDFKB_PDF_PARSER	pymupdf4llm	PDF parser selection
PDFKB_PDF_CHUNKER	langchain	Chunking strategy: langchain, unstructured, semantic
PDFKB_CHUNK_SIZE	1000	LangChain chunk size
PDFKB_CHUNK_OVERLAP	200	LangChain chunk overlap
PDFKB_EMBEDDING_MODEL	text-embedding-3-large	OpenAI model
PDFKB_EMBEDDING_BATCH_SIZE	100	Embedding batch size
PDFKB_MAX_PARALLEL_PARSING	1	Max concurrent PDF parsing operations
PDFKB_MAX_PARALLEL_EMBEDDING	1	Max concurrent embedding operations
PDFKB_BACKGROUND_QUEUE_WORKERS	2	Number of background processing workers
PDFKB_THREAD_POOL_SIZE	1	Thread pool size for CPU-intensive tasks
PDFKB_VECTOR_SEARCH_K	5	Default search results
PDFKB_FILE_SCAN_INTERVAL	60	File monitoring interval
PDFKB_LOG_LEVEL	INFO	Logging level
PDFKB_WEB_ENABLE	false	Enable/disable web interface
PDFKB_WEB_PORT	8080	Web server port
PDFKB_WEB_HOST	localhost	Web server host
PDFKB_WEB_CORS_ORIGINS	http://localhost:3000,http://127.0.0.1:3000	CORS allowed origins (comma-separated)

Parser Comparison Details

Feature	PyMuPDF4LLM	Marker	MinerU	Docling	LLM
Speed	Fastest	Medium	Fast (GPU)	Medium	Slowest
Memory	Lowest	Medium	High	Medium	Lowest
Tables	Basic	Good	Excellent	Excellent	Excellent
Formulas	Basic	Good	Excellent	Good	Excellent
Images	Basic	Good	Good	Excellent	Excellent
Setup	Simple	Simple	Moderate	Simple	Simple
Cost	Free	Free	Free	Free	API costs

Chunking Strategies

LangChain (PDFKB_PDF_CHUNKER=langchain):

• Header-aware splitting with MarkdownHeaderTextSplitter
• Configurable via PDFKB_CHUNK_SIZE and PDFKB_CHUNK_OVERLAP
• Best for customizable chunking
• Default and installed with base package

Semantic (PDFKB_PDF_CHUNKER=semantic) 🆕 NEW:

• Advanced semantic chunking using LangChain's SemanticChunker
• Groups semantically related content together using embedding similarity
• Four breakpoint detection methods: percentile, standard_deviation, interquartile, gradient
• Preserves context and improves retrieval quality by 40%
• Install extra: pip install "pdfkb-mcp[semantic]" to enable
• Configurable via environment variables (see Semantic Chunking section)
• Best for documents requiring high context preservation

Unstructured (PDFKB_PDF_CHUNKER=unstructured):

• Intelligent semantic chunking with unstructured library
• Zero configuration required
• Install extra: pip install "pdfkb-mcp[unstructured_chunker]" to enable
• Best for document structure awareness

First-run notes

• On the first run, the server initializes caches and vector store and logs selected components:
  • Parser: PyMuPDF4LLM (default)
  • Chunker: LangChain (default)
  • Embedding Model: text-embedding-3-large (default)
• If you select a parser/chunker that isn’t installed, the server logs a warning with the exact install command and falls back to the default components instead of exiting.

Troubleshooting Guide

API Key Issues:

1. Verify key format starts with sk-
2. Check account has sufficient credits
3. Test connectivity: curl -H "Authorization: Bearer $PDFKB_OPENAI_API_KEY" https://api.openai.com/v1/models

Parser Installation Issues:

1. MinerU: pip install mineru[all] and verify mineru --version
2. Docling: pip install docling for basic, pip install pdfkb-mcp[docling-complete] for all features
3. LLM: Requires PDFKB_OPENROUTER_API_KEY environment variable

Performance Optimization:

1. Speed: Use pymupdf4llm parser (fastest, low memory footprint)
2. Memory: Reduce PDFKB_EMBEDDING_BATCH_SIZE and PDFKB_CHUNK_SIZE; use pypdfium backend for Docling
3. Quality: Use mineru with GPU (>10K tokens/s on RTX 4090) or marker for balanced quality
4. Tables: Use docling with PDFKB_DOCLING_TABLE_MODE=ACCURATE or marker with LLM mode
5. Batch Processing: Use marker on H100 (~25 pages/s) or mineru with sglang acceleration

For additional support, see implementation details in src/pdfkb/main.py and src/pdfkb/config.py.