LumenX-MCP Legal Spend Intelligence Server

A Model Context Protocol (MCP) server for intelligent legal spend analysis across multiple data sources. Part of the LumenX suite powered by DatSciX.

🚀 Features

Multi-Source Integration: Connect to multiple data sources simultaneously
- LegalTracker API integration
- Database support (PostgreSQL, SQL Server, Oracle)
- File imports (CSV, Excel)
Comprehensive Analytics:
- Spend summaries by period, department, practice area
- Vendor performance analysis
- Budget vs. actual comparisons
- Transaction search capabilities
MCP Compliant: Full implementation of Model Context Protocol standards
Async Architecture: High-performance asynchronous data processing
Extensible Design: Easy to add new data sources and analytics

📋 Prerequisites

Python 3.10 or higher
Access to one or more supported data sources
MCP-compatible client (e.g., Claude Desktop)

🛠️ Installation

Using pip

pip install legal-spend-mcp

From Source

# Clone the repository
git clone https://github.com/DatSciX-CEO/LumenX-MCP.git
cd LumenX-MCP

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -e .

Using uv (recommended)

# Install uv if not already installed
pip install uv

# Clone and install
git clone https://github.com/DatSciX-CEO/LumenX-MCP.git
cd LumenX-MCP
uv pip install -e .

⚙️ Configuration

Copy the environment template:

cp .env.template .env

Edit .env with your data source credentials:

# Enable the data sources you want to use
LEGALTRACKER_ENABLED=true
LEGALTRACKER_API_KEY=your_api_key_here
LEGALTRACKER_BASE_URL=https://api.legaltracker.com

# Database connections (optional)
SAP_ENABLED=false
SAP_HOST=your_sap_host
SAP_PORT=1433
SAP_DATABASE=your_database
SAP_USER=your_username
SAP_PASSWORD=your_password

# File sources (optional)
CSV_ENABLED=true
CSV_FILE_PATH=/path/to/legal_spend.csv

🚀 Quick Start

Running the Server

# Using the installed command
legal-spend-mcp

# Or using Python
python -m legal_spend_mcp.server

Configure with Claude Desktop

Add to your Claude Desktop configuration (claude_config.json):

{
  "mcpServers": {
    "legal-spend": {
      "command": "legal-spend-mcp",
      "env": {
        "LEGALTRACKER_ENABLED": "true",
        "LEGALTRACKER_API_KEY": "your_api_key"
      }
    }
  }
}

📚 Available Tools

get_legal_spend_summary

Get aggregated spend data with filtering options.

Parameters:

start_date (required): Start date in YYYY-MM-DD format
end_date (required): End date in YYYY-MM-DD format
department (optional): Filter by department
practice_area (optional): Filter by practice area
vendor (optional): Filter by vendor name
data_source (optional): Query specific data source

Example:

result = await get_legal_spend_summary(
    start_date="2024-01-01",
    end_date="2024-12-31",
    department="Legal"
)

get_vendor_performance

Analyze performance metrics for a specific vendor.

Parameters:

vendor_name (required): Name of the vendor
start_date (required): Start date in YYYY-MM-DD format
end_date (required): End date in YYYY-MM-DD format
include_benchmarks (optional): Include industry comparisons

get_budget_vs_actual

Compare actual spending against budgeted amounts.

Parameters:

department (required): Department name
start_date (required): Start date in YYYY-MM-DD format
end_date (required): End date in YYYY-MM-DD format
budget_amount (required): Budget amount to compare

search_legal_transactions

Search for specific transactions across all data sources.

Parameters:

search_term (required): Search query
start_date (optional): Start date filter
end_date (optional): End date filter
min_amount (optional): Minimum amount filter
max_amount (optional): Maximum amount filter
limit (optional): Maximum results (default: 50)

📊 Resources

The server provides several MCP resources for reference data:

legal_vendors: List of all vendors across data sources
data_sources: Status and configuration of data sources
spend_categories: Available categories and practice areas
spend_overview://recent: Recent spend activity overview

🔌 Supported Data Sources

LegalTracker API

Real-time invoice and matter data
Vendor management information
Practice area classifications

Databases

PostgreSQL: Full support for legal spend tables
SQL Server: Compatible with SAP and other ERP systems
Oracle: Enterprise financial system integration

File Imports

CSV: Standard comma-separated values
Excel: .xlsx files with configurable sheet names

📝 Data Model

The server uses a standardized data model for legal spend records:

@dataclass
class LegalSpendRecord:
    invoice_id: str
    vendor_name: str
    vendor_type: VendorType
    matter_id: Optional[str]
    matter_name: Optional[str]
    department: str
    practice_area: PracticeArea
    invoice_date: date
    amount: Decimal
    currency: str
    expense_category: str
    description: str
    # ... additional fields

🧪 Testing

Run the test suite:

# Run all tests
pytest

# Run with coverage
pytest --cov=legal_spend_mcp

# Run specific test file
pytest tests/test_server.py

🤝 Contributing

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

Please ensure:

All tests pass
Code follows the project style guide
Documentation is updated
Commit messages are descriptive

📄 License

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

🙏 Acknowledgments

Built on the Model Context Protocol
Powered by DatSciX
Part of the LumenX suite of enterprise tools

📞 Support

Issues: GitHub Issues
Discussions: GitHub Discussions
Email: patrick@datscix.com

🗺️ Roadmap

Additional data source integrations
Machine learning-based spend predictions
Automated anomaly detection
Enhanced benchmark analytics
GraphQL API support
Real-time notifications

This server cannot be installed

security - not tested

license - permissive license

quality - not tested

How are these scores calculated?

MCP server that enables intelligent analysis of legal spend data across multiple sources (LegalTracker, databases, CSV/Excel files), providing features like spend summaries, vendor performance analysis, and budget comparisons.

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