Shubhlakhia007/mcp-server

MCP Server with nmap Integration

A Model Context Protocol (MCP) server that integrates with LMStudio and provides OpenAI-compatible endpoints. This server includes natural language processing for nmap commands and scan result analysis.

Features

• ✨ Natural Language → nmap Converter (NL2nmap)
• 📊 Scan Result Summarizer & Recommendations
• 🤖 Interactive Elicitation Flow
• 📅 Scheduled/Queued Scans
• 🔒 LLM Safety & Sandboxing
• 🧪 Comprehensive Testing

Quick Start

1. Clone the repository:

git clone https://github.com/YOUR_USERNAME/mcp-server.git
cd mcp-server

2. Create a virtual environment and install dependencies:

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

3. Configure the environment: Create a .env file with:

LLM_API_KEY=sk-no-key-required
LLM_BASE_URL=http://127.0.0.1:1234/v1
MODEL_NAME=qwen2.5:3b-instruct-q4_K_M
MCP_SERVER_PORT=8001

4. Start the server:

python -m uvicorn app.main:app --host 0.0.0.0 --port 8001 --reload

API Endpoints

Chat Completions

• POST /v1/chat/completions
  • OpenAI-compatible chat completions endpoint
  • Supports both streaming and non-streaming responses

Health Check

• GET /v1/health
  • Returns server status and configuration

Example Usage

# Basic chat completion request
curl -X POST http://localhost:8001/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen2.5:3b-instruct-q4_K_M",
    "messages": [{"role": "user", "content": "Say hello!"}],
    "stream": false
  }'

Development

The project follows a modular architecture:

• app/: Main application package
  • config/: Configuration settings
  • routes/: API endpoints
  • schemas/: Data models
  • services/: Business logic
  • utils/: Helper functions

Contributing

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

License

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