Document Analysis Agent

A powerful document analysis system built on the Model Context Protocol (MCP) that provides advanced document processing, analysis, and information extraction capabilities using Claude 3 Opus.

Features

• 📄 Document Analysis: Extract key information, entities, dates, and monetary values
• 📊 Document Summarization: Generate summaries at different detail levels
• 🔍 Entity Search: Find documents mentioning specific entities
• 📁 Batch Processing: Process multiple documents efficiently
• 🤖 AI-Powered: Leverages Claude 3 Opus for intelligent analysis
• 🔄 MCP Integration: Built on the Model Context Protocol for standardized communication

Prerequisites

• Python 3.11 or higher
• uv for dependency management
• Anthropic API key for Claude 3 access

Installation

1. Clone the repository:

git clone https://github.com/yourusername/doc-analysis-agent.git
cd doc-analysis-agent

2. Create a virtual environment and install dependencies using uv:

uv venv
source .venv/bin/activate  # On Unix/macOS
uv pip install -e .

3. Set up your environment variables:

cp .env.example .env

Edit .env and add your Anthropic API key:

ANTHROPIC_API_KEY=your-api-key-here

Usage

The Document Analysis Agent provides several ways to process and analyze documents. Here are some common use cases:

Basic Document Analysis

from docanalysis.mcp.server import FastMCPDocumentAnalysisServer

# Initialize the server
server = FastMCPDocumentAnalysisServer()
await server.initialize()

# Analyze a document
result = await server.analyze_document({
    "content": "Your document content here",
    "metadata": {
        "type": "contract",
        "title": "Sample Document"
    }
})

Check out the examples directory for more detailed examples:

• Basic Analysis: Simple document analysis
• Batch Processing: Process multiple documents
• Document Summary: Generate summaries
• Entity Search: Search for entities

API Reference

Document Analysis

• analyze_document(document): Analyze a single document
• analyze_documents(documents): Process multiple documents
• summarize_document(document, detail_level): Generate document summary
• extract_info(document, info_types): Extract specific information
• find_documents_by_entity(entity): Search for documents by entity

Document Types

The system supports various document types with specialized analysis:

• Contracts
• Reports
• Invoices
• Amendments
• Press Releases
• And more...

Development

Running Tests

python -m pytest tests/ -v

# If using hatch
hatch run test:test

Docker Support

Build and run using Docker:

docker-compose up --build

Contributing

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Built with FastMCP
• Powered by Anthropic's Claude 3