qwen3-coder-mcp

keithah/qwen3-coder-mcp

3.2

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Qwen3-Coder MCP Server integrates the Qwen3-Coder model with Claude Code using the Model Context Protocol, optimized for 64GB RAM systems.

Tools
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Resources
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Prompts
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Qwen3-Coder MCP Server for Claude Code

This setup integrates Qwen3-Coder (30B parameter model) with Claude Code via the Model Context Protocol (MCP), optimized for 64GB RAM systems.

Features

  • Qwen3-Coder 30B: Latest and most powerful Qwen Coder model with exceptional coding capabilities
  • 64GB RAM Optimized: Configuration tuned for maximum performance on high-memory systems
  • MCP Integration: Seamless integration with Claude Code through 5 specialized tools
  • Advanced Settings: Flash attention, optimized KV cache, and parallel processing

Optimization Settings

The setup includes these optimizations for your 64GB RAM:

  • OLLAMA_NUM_PARALLEL=8: Handle 8 parallel requests
  • OLLAMA_MAX_LOADED_MODELS=4: Keep 4 models in memory simultaneously
  • OLLAMA_FLASH_ATTENTION=1: Enable efficient attention mechanism
  • OLLAMA_KV_CACHE_TYPE=q8_0: High-quality 8-bit cache
  • OLLAMA_KEEP_ALIVE=24h: Keep models loaded for 24 hours

Available Tools

1. qwen3_code_review

Reviews code for quality, bugs, and best practices.

Parameters:

  • code (required): The code to review
  • language (optional): Programming language

2. qwen3_code_explain

Provides detailed explanations of how code works.

Parameters:

  • code (required): The code to explain
  • language (optional): Programming language

3. qwen3_code_generate

Generates new code based on requirements.

Parameters:

  • prompt (required): Description of what to generate
  • language (optional): Target programming language

4. qwen3_code_fix

Fixes bugs and issues in existing code.

Parameters:

  • code (required): The buggy code
  • error (optional): Error message or description
  • language (optional): Programming language

5. qwen3_code_optimize

Optimizes code for performance, memory, or readability.

Parameters:

  • code (required): The code to optimize
  • criteria (optional): Optimization criteria
  • language (optional): Programming language

Quick Start

1. Start the Optimized Server

cd /Users/keith/qwencoder
./start-qwen3-optimized.sh

2. Restart Claude Code

Close and reopen Claude Code to load the MCP server configuration.

3. Use in Claude Code

The tools will be automatically available in your Claude Code sessions. You can use them by referencing the tool names in your conversations.

Manual Commands

Start Ollama with optimizations:

OLLAMA_NUM_PARALLEL=8 OLLAMA_MAX_LOADED_MODELS=4 OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama serve

Test the model directly:

ollama run qwen3-coder:30b "Write a Python function to calculate factorial"

Test the MCP server:

node qwen3-mcp-server.js

Troubleshooting

If Claude Code doesn't see the MCP server:

  1. Check that the config.json has the correct path
  2. Restart Claude Code completely
  3. Verify Ollama is running: ollama list

If the model is slow:

  1. Ensure you have enough RAM available
  2. Check that OLLAMA_FLASH_ATTENTION=1 is set
  3. Monitor system resources with Activity Monitor

If tools aren't working:

  1. Test Ollama directly: ollama run qwen3-coder:30b "test"
  2. Check MCP server logs in Console.app
  3. Verify the Node.js dependencies are installed

Files Structure

/Users/keith/qwencoder/
ā”œā”€ā”€ qwen3-mcp-server.js          # MCP server implementation
ā”œā”€ā”€ package.json                 # Node.js dependencies
ā”œā”€ā”€ start-qwen3-optimized.sh     # Optimized startup script
└── README.md                    # This file

Configuration Files

  • Claude Config: /Users/keith/Library/Application Support/Claude/config.json
  • MCP Server: /Users/keith/qwencoder/qwen3-mcp-server.js

Performance Notes

With 64GB RAM, you can:

  • Keep multiple large models loaded simultaneously
  • Handle numerous parallel requests
  • Use high-quality cache settings for better performance
  • Run for extended periods without memory issues

The Qwen3-Coder 30B model uses approximately 18GB of RAM when loaded, leaving plenty of room for other applications and additional models.