QuantConnect PDF MCP Server

An advanced Model Context Protocol (MCP) server that provides intelligent search and retrieval capabilities for QuantConnect PDF documentation. This server converts PDFs to searchable markdown format and provides fast, context-aware search using TF-IDF scoring and proximity matching.

Features

• Intelligent PDF Processing: Automatically converts PDFs to structured markdown with proper formatting
• Fast Search Index: Uses inverted index with TF-IDF scoring for relevant results
• Context-Aware Results: Returns relevant excerpts with highlighted matches
• Caching System: Avoids reprocessing unchanged PDFs for better performance
• Proximity Matching: Boosts results where query terms appear close together
• Three MCP Tools: Search, list documents, and retrieve full content

Project Structure

QuantConnectServer/
├── server.py           # Main MCP server with enhanced search
├── convert_pdfs.py     # Standalone PDF conversion utility
├── requirements.txt    # Python dependencies
├── README.md          # This documentation
├── env/               # Python virtual environment
└── quantconnect-docs/ # PDF documents and converted markdown
    ├── Quantconnect-Local-Platform-Python-2.pdf
    ├── Quantconnect-Writing-Algorithms-Python-2.pdf
    └── markdown/      # Auto-generated markdown files
        ├── .pdf_cache.json      # Processing cache
        ├── .search_index.pkl    # Search index cache
        └── *.md files           # Converted documents

Installation

Prerequisites

• Python 3.8 or higher
• pip package manager

Step 1: Install Dependencies

Install required packages:

pip install -r requirements.txt

The `requirements.txt` includes:

• `mcp` - Model Context Protocol library
• `PyPDF2` - PDF text extraction
• `asyncio` - Asynchronous processing

Step 2: Prepare Your Environment

Create a virtual environment (recommended):

python -m venv env
source env/bin/activate  # On Windows: env\Scripts\activate
pip install -r requirements.txt

Configuration

Claude Desktop Setup

Find your Claude Desktop configuration file:

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

Add this configuration (adjust paths to match your system):

{
  "mcpServers": {
    "quantconnect-pdf-server": {
      "command": "/path/to/your/project/env/bin/python3",
      "args": ["/path/to/your/project/server.py"],
      "env": {
        "QUANTCONNECT_PDF_FOLDER": "/path/to/your/project/quantconnect-docs",
        "QUANTCONNECT_MARKDOWN_FOLDER": "/path/to/your/project/quantconnect-docs/markdown"
      }
    }
  }
}

Environment Variables

• `QUANTCONNECT_PDF_FOLDER`: Directory containing your PDF files (required)
• `QUANTCONNECT_MARKDOWN_FOLDER`: Directory for converted markdown files (optional, defaults to `PDF_FOLDER/markdown`)

Usage

Starting the Server

1. Standalone testing:

export QUANTCONNECT_PDF_FOLDER="/path/to/your/pdfs"
   python server.py

2. With Claude Desktop: Restart Claude Desktop after configuration to load the MCP server

3. Manual PDF conversion (optional):

python convert_pdfs.py [pdf_folder] [markdown_folder]

Testing the Integration

Test in Claude by asking:

• "Can you list the available QuantConnect documents?"
• "Search for information about backtesting in the QuantConnect docs"
• "What does the QuantConnect documentation say about indicators?"
• "Show me page 5 of the Local Platform documentation"

Available MCP Tools

The server provides three powerful tools accessible through Claude:

1. `search_quantconnect_docs`

Purpose: Intelligent search through all QuantConnect documentation

Parameters:

• `query` (required): Search terms or topic to find
• `max_results` (optional): Number of results to return (default: 5)

Features:

• TF-IDF scoring for relevance ranking
• Proximity matching for multi-word queries
• Context extraction with highlighted matches
• Returns document excerpts with page numbers

2. `list_quantconnect_docs`

Purpose: List all available PDF documents in the collection

Parameters: None

Returns: Complete catalog of processed documents with metadata

3. `get_document_content`

Purpose: Retrieve full content from specific documents

Parameters:

• `filename` (required): Document name (with or without .md extension)
• `page_number` (optional): Specific page to retrieve

Use cases: Reading complete sections, accessing specific pages, extracting code examples

Technical Architecture

Search Engine

• Inverted Index: Maps words to document locations for fast lookup
• TF-IDF Scoring: Balances term frequency with document rarity
• Proximity Boosting: Enhances results where query terms appear together
• Context Extraction: Provides relevant snippets around matches

Caching System

• PDF Processing Cache: Avoids reprocessing unchanged files using MD5 hashes
• Search Index Cache: Persists search index for faster startup
• Incremental Updates: Only processes new or modified PDFs

Performance Features

• Asynchronous Processing: Non-blocking PDF conversion and indexing
• Background Initialization: Server starts immediately while processing continues
• Efficient Storage: Markdown conversion reduces memory usage vs. raw PDF text

Troubleshooting

Common Issues

1. Server not connecting

2. PDFs not loading

3. Search returning no results

4. Performance issues

Debug Mode

Run the server with debug output:

export QUANTCONNECT_PDF_FOLDER="/path/to/pdfs"
python server.py 2>&1 | tee server.log

Advanced Usage

Bulk PDF Processing

Process all PDFs without starting the server:

python convert_pdfs.py ./quantconnect-docs ./quantconnect-docs/markdown

Custom Search Queries

The search supports various query types:

• Single terms: `backtesting`
• Multi-word queries: `custom indicator development`
• Technical terms: `OnData event handler`
• Code concepts: `Algorithm.Initialize method`

Integration Examples

Ask Claude sophisticated questions like:

"Using the QuantConnect docs, show me step-by-step how to create a custom indicator with examples"

"What are all the different order types available and when should I use each one?"

"Find code examples of universe selection and explain the different approaches"

"Compare the local platform setup process with cloud deployment according to the documentation"

Contributing

To extend the server:

1. Add new document formats: Extend the conversion system in `server.py:236`

2. Improve search: Enhance the `SearchIndex` class for semantic search

3. Add specialized tools: Create domain-specific search functions

4. Performance optimization: Implement parallel processing or database storage

Version History

• v0.3.0: Enhanced search with TF-IDF scoring and proximity matching
• v0.2.0: Added caching system and background processing
• v0.1.0: Basic PDF to markdown conversion and simple search