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 semantic search capabilities powered by OpenAI embeddings and ChromaDB vector storage.

Table of Contents

• 🚀 Quick Start
• 🏗️ Architecture Overview
• 🎯 Parser Selection Guide
• ⚙️ Configuration
• 🖥️ MCP Client Setup
• 📊 Performance & Troubleshooting
• 🔧 Advanced Configuration
• 📚 Appendix

🚀 Quick Start

Step 1: Install the Server

uvx pdfkb-mcp

Step 2: Configure Your MCP Client

Claude Desktop (Most Common):

Configuration file locations:

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

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "KNOWLEDGEBASE_PATH": "/Users/yourname/Documents/PDFs"
      },
      "transport": "stdio",
      "autoRestart": true
    }
  }
}

VS Code (Native MCP) - Create .vscode/mcp.json in workspace:

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

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

🏗️ Architecture Overview

MCP Integration

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   MCP Client    │    │   MCP Client     │    │   MCP Client    │
│ (Claude Desktop)│    │(VS Code/Continue)|    │   (Other)       │
└─────────┬───────┘    └─────────┬────────┘    └─────────┬───────┘
          │                      │                       │
          └──────────────────────┼───────────────────────┘
                                 │
                    ┌────────────┴────────────┐
                    │    Model Context        │
                    │    Protocol (MCP)       │
                    │    Standard Layer       │
                    └────────────┬────────────┘
                                 │
          ┌──────────────────────┼───────────────────────┐
          │                      │                       │
┌─────────┴───────┐    ┌─────────┴────────┐    ┌─────────┴───────┐
│ PDF KB Server   │    │  Other MCP       │    │  Other MCP      │
│ (This Server)   │    │  Server          │    │  Server         │
└─────────────────┘    └──────────────────┘    └─────────────────┘

Available Tools & Resources

Tools (Actions your client can perform):

• add_document(path, metadata?) - Add PDF to knowledgebase
• search_documents(query, limit=5, metadata_filter?) - Semantic search across PDFs
• list_documents(metadata_filter?) - List all documents with metadata
• remove_document(document_id) - Remove document from knowledgebase</search> </search_and_replace>

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

🎯 Parser Selection Guide

Decision Tree

Document Type & Priority?
├── 🏃 Speed Priority → PyMuPDF4LLM (fastest processing, low memory)
├── 📚 Academic Papers → MinerU (fast with GPU, excellent formulas)
├── 📊 Business Reports → Docling (medium speed, best tables)
├── ⚖️ Balanced Quality → Marker (medium speed, good structure)
└── 🎯 Maximum Accuracy → LLM (slow, vision-based API calls)
```</search>
</search_and_replace>

### Performance Comparison

| Parser | Processing Speed | Memory | Text Quality | Table Quality | Best For |
|--------|------------------|--------|--------------|---------------|----------|
| **PyMuPDF4LLM** | **Fastest** | Low | Good | Basic | Speed priority |
| **MinerU** | Fast (with GPU) | High | Excellent | Excellent | Scientific papers |
| **Docling** | Medium | Medium | Excellent | **Excellent** | Business documents |
| **Marker** | Medium | Medium | Excellent | Good | **Balanced (default)** |
| **LLM** | Slow | Low | Excellent | Excellent | Maximum accuracy |</search>
</search_and_replace>

*Benchmarks from research studies and technical reports*

## ⚙️ Configuration

### Tier 1: Basic Configurations (80% of users)

**Default (Recommended)**:
```json
{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDF_PARSER": "marker"
      },
      "transport": "stdio"
    }
  }
}

Speed Optimized:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDF_PARSER": "pymupdf4llm",
        "CHUNK_SIZE": "800"
      },
      "transport": "stdio"
    }
  }
}

Memory Efficient:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDF_PARSER": "pymupdf4llm",
        "EMBEDDING_BATCH_SIZE": "50"
      },
      "transport": "stdio"
    }
  }
}

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

Academic Papers:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDF_PARSER": "mineru",
        "CHUNK_SIZE": "1200"
      },
      "transport": "stdio"
    }
  }
}

Business Documents:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDF_PARSER": "docling",
        "DOCLING_TABLE_MODE": "ACCURATE",
        "DOCLING_DO_TABLE_STRUCTURE": "true"
      },
      "transport": "stdio"
    }
  }
}

Multi-language Documents:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDF_PARSER": "docling",
        "DOCLING_OCR_LANGUAGES": "en,fr,de,es",
        "DOCLING_DO_OCR": "true"
      },
      "transport": "stdio"
    }
  }
}

Maximum Quality:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "OPENROUTER_API_KEY": "sk-or-v1-abc123def456ghi789...",
        "PDF_PARSER": "llm",
        "LLM_MODEL": "anthropic/claude-3.5-sonnet",
        "EMBEDDING_MODEL": "text-embedding-3-large"
      },
      "transport": "stdio"
    }
  }
}

Essential Environment Variables

Variable	Default	Description
OPENAI_API_KEY	required	OpenAI API key for embeddings
KNOWLEDGEBASE_PATH	./pdfs	Directory containing PDF files
CACHE_DIR	./.cache	Cache directory for processing
PDF_PARSER	marker	Parser: marker, pymupdf4llm, mineru, docling, llm
CHUNK_SIZE	1000	Target chunk size for LangChain chunker
EMBEDDING_MODEL	text-embedding-3-large	OpenAI embedding model

🖥️ 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": {
        "OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "KNOWLEDGEBASE_PATH": "/Users/yourname/Documents/PDFs",
        "CACHE_DIR": "/Users/yourname/Documents/PDFs/.cache"
      },
      "transport": "stdio",
      "autoRestart": true
    }
  }
}

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": {
        "OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "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": {
        "OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "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</search> </search_and_replace>

Generic MCP Client

Standard Configuration Template:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "OPENAI_API_KEY": "required",
        "KNOWLEDGEBASE_PATH": "required-absolute-path",
        "PDF_PARSER": "optional-default-marker"
      },
      "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"
    }
  }
}

Processing too slow:

// Switch to faster parser
{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "OPENAI_API_KEY": "sk-key",
        "PDF_PARSER": "pymupdf4llm"
      },
      "transport": "stdio"
    }
  }
}

Memory issues:

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

Poor table extraction:

// Use table-optimized parser
{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "OPENAI_API_KEY": "sk-key",
        "PDF_PARSER": "docling",
        "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": {
        "OPENAI_API_KEY": "sk-key",
        "PDF_PARSER": "mineru",
        "MINERU_LANG": "en",
        "MINERU_METHOD": "auto",
        "MINERU_VRAM": "16"
      },
      "transport": "stdio"
    }
  }
}

LLM Parser Configuration:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "OPENAI_API_KEY": "sk-key",
        "OPENROUTER_API_KEY": "sk-or-v1-abc123def456ghi789...",
        "PDF_PARSER": "llm",
        "LLM_MODEL": "google/gemini-2.5-flash-lite",
        "LLM_CONCURRENCY": "5",
        "LLM_DPI": "150"
      },
      "transport": "stdio"
    }
  }
}

Performance Tuning

High-Performance Setup:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "OPENAI_API_KEY": "sk-key",
        "PDF_PARSER": "mineru",
        "KNOWLEDGEBASE_PATH": "/Volumes/FastSSD/Documents/PDFs",
        "CACHE_DIR": "/Volumes/FastSSD/Documents/PDFs/.cache",
        "EMBEDDING_BATCH_SIZE": "200",
        "VECTOR_SEARCH_K": "15",
        "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 PDF_PARSER → Full reset (parsing + chunking + embeddings)
• Changing PDF_CHUNKER → Partial reset (chunking + embeddings)
• Changing EMBEDDING_MODEL → Minimal reset (embeddings only)

📚 Appendix

Installation Options

Primary (Recommended):

uvx pdfkb-mcp

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[langchain]  # LangChain chunker

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
OPENAI_API_KEY	required	OpenAI API key for embeddings
OPENROUTER_API_KEY	optional	Required for LLM parser
KNOWLEDGEBASE_PATH	./pdfs	PDF directory path
CACHE_DIR	./.cache	Cache directory
PDF_PARSER	marker	PDF parser selection
PDF_CHUNKER	unstructured	Chunking strategy
CHUNK_SIZE	1000	LangChain chunk size
CHUNK_OVERLAP	200	LangChain chunk overlap
EMBEDDING_MODEL	text-embedding-3-large	OpenAI model
EMBEDDING_BATCH_SIZE	100	Embedding batch size
VECTOR_SEARCH_K	5	Default search results
FILE_SCAN_INTERVAL	60	File monitoring interval
LOG_LEVEL	INFO	Logging level

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 (PDF_CHUNKER=langchain):

• Header-aware splitting with MarkdownHeaderTextSplitter
• Configurable via CHUNK_SIZE and CHUNK_OVERLAP
• Best for customizable chunking

Unstructured (PDF_CHUNKER=unstructured):

• Intelligent semantic chunking with unstructured library
• Zero configuration required
• Best for document structure awareness

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 $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 OPENROUTER_API_KEY environment variable

Performance Optimization:

1. Speed: Use pymupdf4llm parser
2. Memory: Reduce EMBEDDING_BATCH_SIZE and CHUNK_SIZE
3. Quality: Use mineru (GPU) or docling (CPU)
4. Tables: Use docling with DOCLING_TABLE_MODE=ACCURATE

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