Torque by udayan-navi - MCP Server

Torque MCP Server 🚀

A powerful Python-based Model Context Protocol (MCP) server with advanced capabilities for file operations, system management, data processing, security tools, and much more.

✨ Features

🗂️ File Operations

Read/write files with automatic encoding detection
Advanced file search with regex patterns
Backup support and security validation
Path traversal protection

🔧 System Management

Execute commands with timeout and security controls
Process monitoring and management
Comprehensive system information
Real-time resource monitoring

📊 Data Processing

CSV processing with pandas (filter, sort, analyze)
JSON validation, formatting, and querying
Large file handling with size limits
Statistical analysis and transformations

🌐 Web & Network

Advanced web scraping with BeautifulSoup
File downloads with progress tracking
Network port scanning
Ping and connectivity testing

🔒 Security Tools

File hashing (MD5, SHA1, SHA256, SHA512)
Data encryption/decryption with Fernet
Secure key derivation
Path validation and access controls

🔄 Git Integration

Detailed repository status
Advanced commit operations
Branch management (create, delete, switch)
Remote tracking information

🚀 Installation

Prerequisites

Python 3.9 or higher
Git (for version control features)

Setup

Clone the repository:

git clone <repository-url>
cd torque-mcp-server

Install dependencies:

pip install -e .

Install development dependencies (optional):

pip install -e ".[dev]"

Setup pre-commit hooks (optional):

pre-commit install

🎯 Usage

Command Line

# Run the server
torque-mcp

# Run with custom configuration
torque-mcp --config config.json --log-level DEBUG

# Run with file logging
torque-mcp --log-file server.log

Python API

from torque_mcp.main import TorqueMCPServer
from torque_mcp.config import Config

config = Config()
server = TorqueMCPServer(config)

# Analyze claims patterns
claims_analysis = await server.call_tool("insurance_claims_analysis", {
    "catalog": "insurance", 
    "schema": "claims",
    "days_back": 90
})

Risk Assessment

# Assess specific policy risk
risk_report = await server.call_tool("insurance_risk_assessment", {
    "policy_number": "POL-12345678-CA",
    "catalog": "insurance"
})

# Premium optimization analysis
premium_analysis = await server.call_tool("insurance_premium_optimization", {
    "catalog": "insurance",
    "schema": "policies"
})

Claims Processing

# Process claim through workflow
claim_workflow = await server.call_tool("process_claim_workflow", {
    "claim_data": {
        "claim_number": "CLM-2024-001234",
        "policy_number": "POL-12345678-CA",
        "incident_date": "2024-01-15",
        "claim_amount": 15000,
        "description": "Collision with another vehicle"
    }
})

🏢 Insurance Business Rules

Policy Validation

Policy Number Format: POL-12345678-XX
VIN Validation: 17-character alphanumeric format
Premium Ranges: $200 - $10,000 annual
Policy Duration: 30-366 days
Required Coverage: Liability and Personal Injury minimum

Claims Processing

High-Value Claims: >$25,000 require senior adjuster
Fraud Detection: Multi-factor scoring system
Processing Timeline: 3-40 business days based on complexity
Auto-Approval: Claims <$1,000 with low fraud score

Risk Assessment

Driver Age Factors: <25 and >65 higher risk
Experience Requirements: <3 years increases premium
Vehicle Age: Pre-2015 vehicles flagged for review
Claims History: >2 claims indicate higher risk

🔒 Security & Compliance

file://.gitignore - Git ignore rules
config://server - Current server configuration

🤝 Contributing

Fork the repository
Create a feature branch
Make your changes
Add tests for new functionality
Run the test suite
Submit a pull request

📄 License

MIT License - see LICENSE file for details

🆘 Support

Issues: Report bugs and request features on GitHub
Documentation: Check the inline documentation in the code
Examples: See the examples/ directory for usage examples

Built with ❤️ using Python and the Model Context Protocol