Qwen3 Server

A Model Context Protocol (MCP) server ecosystem providing access to multiple AI models optimized for different tasks: code generation, vision analysis, and complex reasoning.

🚀 Quick Start

# Automated setup
./setup.sh

# Start default server
python src/main.py

# Or use ephemeral model switching
ask-qwen3 "Write a Python function"    # Code generation
ask-vision "Analyze this image"        # Visual analysis  
ask-ministral "Solve this equation"     # Complex reasoning

📚 Documentation

Essential Guides

• Setup Guide - Complete installation and configuration
• Usage Guide - Workflows, examples, and best practices
• Models Reference - Model capabilities and configurations
• Agent Guide - Warp agent integration guidance

Quick Navigation

• 🏗️ Getting Started: Setup Guide → Usage Guide
• 🤖 Model Selection: See Models Reference
• 🔧 Troubleshooting: Check Setup Guide or Usage Guide
• 🎯 Specific Tasks: Browse Usage Guide

🌟 Features

Multi-Model Ecosystem

• Qwen3-Coder-Next: Code generation, debugging, technical writing
• Qwen3-VL-8B: Image analysis, UI review, document OCR
• Qwen3-30B: Complex reasoning with thinking mode
• Ministral-3-14B: Mathematical reasoning and logical analysis

Flexible Hosting

• Ollama: Local model serving (recommended)
• HTTP API: Remote model endpoints
• Transformers: Direct model loading
• Ephemeral Switching: Dynamic model selection

Developer Experience

• MCP Compliance: Full Model Context Protocol support
• Shell Integration: Quick aliases and commands
• Warp Integration: Native Warp agent support
• Multi-Transport: stdio and HTTP transports
• Thinking Mode: Detailed reasoning visualization

🎯 Use Cases

⚡ Quick Commands

Model Switching

mcp-qwen3     # Code-focused development
mcp-vision    # Visual analysis tasks
mcp-ministral # Reasoning and mathematics
mcp-all       # Enable all models
mcp-clean     # Reset to clean state

One-Shot Tasks

ask-qwen3 "Write a REST API endpoint"
ask-vision "What's wrong with this UI?"
ask-ministral "Prove this theorem"

Server Management

# Start with specific model
python src/main.py --model-method ollama --ollama-model qwen3:30b-a3b

# Start with HTTP endpoint
python src/main.py --model-method http --http-model qwen/qwen3-coder-next

# Enable debug logging
python src/main.py --log-level DEBUG

🔧 System Requirements

• Python: 3.10+ (3.12+ recommended)
• Memory: 16GB+ RAM (32GB+ for 30B model)
• Network: Access to HTTP endpoints or Ollama service
• OS: macOS, Linux, Windows
• Optional: CUDA-compatible GPU for Transformers method

🚦 Health Check

# Check system status
mcp-list

# Test specific model
ask-ministral "Hello, are you working?"

# Verify endpoints
curl -s http://localhost:1234/v1/models

📁 Project Structure

qwen3-mcp-server/
├── docs/                  # 📚 Comprehensive documentation
│   ├── SETUP.md          # Installation and configuration
│   ├── USAGE.md          # Usage patterns and examples
│   └── MODELS.md         # Model reference and capabilities
├── src/                   # 🔧 Core implementation
│   ├── main.py           # Entry point and CLI
│   ├── server.py         # MCP server implementation  
│   ├── model_interface.py # Model hosting abstractions
│   └── config.py         # Configuration management
├── config/                # ⚙️ Model configurations
│   ├── qwen3-coder-http.json
│   ├── qwen3-vl-8b-http.json
│   └── ministral-3-14b-reasoning-http.json
├── scripts/               # 🤖 Automation scripts
│   └── switch-model.sh   # Model switching logic
├── AGENTS.md             # 🤖 Warp agent guidance
├── setup.sh              # 🚀 Automated setup
└── requirements.txt      # 📦 Python dependencies


## 📄 License

MIT License - see [LICENSE](LICENSE) file for details.

## 🙏 Acknowledgments

- [Model Context Protocol](https://modelcontextprotocol.io/) by Anthropic
- [Qwen Team](https://github.com/QwenLM) for the Qwen3 models  
- [Ollama](https://ollama.ai/) for local model hosting
- [Mistral AI](https://mistral.ai/) for the Ministral reasoning model