ai-mldeveloper-tools

Qwen Code

jaggerxtrm

by jaggerxtrm

Enhance your codebase with Qwen Code, a leading code quality analysis tool offering advanced CLI integration and automat

Bridges Qwen's code analysis capabilities through CLI integration, providing file-referenced queries with @filename syntax, automatic model fallback, and configurable execution modes for code review, codebase exploration, and automated refactoring workflows.

github stars

3

0 commentsdiscussion

Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Large context window supportFile-referenced queries with @ syntaxSandbox execution mode

best for

  • / Developers doing code review and analysis
  • / Teams exploring large codebases
  • / Automated refactoring workflows
  • / Safe code testing and execution

capabilities

  • / Query Qwen AI models about code with @filename syntax
  • / Analyze entire codebases with large context windows
  • / Execute code safely in sandbox environments
  • / Switch between multiple Qwen model variants
  • / Control execution with configurable approval modes

what it does

Integrates Qwen AI models with MCP-compatible assistants for code analysis, allowing you to query AI about specific files using @filename syntax and leverage large context windows for codebase exploration.

about

Qwen Code is a community-built MCP server published by jaggerxtrm that provides AI assistants with tools and capabilities via the Model Context Protocol. Enhance your codebase with Qwen Code, a leading code quality analysis tool offering advanced CLI integration and automat It is categorized under ai ml, developer tools. This server exposes 3 tools that AI clients can invoke during conversations and coding sessions.

how to install

You can install Qwen Code in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

Qwen Code is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

Qwen MCP Tool

Model Context Protocol server for Qwen CLI integration. This tool enables AI assistants like Claude to leverage Qwen's powerful code analysis and large context window capabilities through the MCP protocol.

Features

  • Large Context Windows: Leverage Qwen's massive token capacity for analyzing large files and entire codebases
  • File Analysis: Use @filename or @directory syntax to include file contents in your queries
  • Sandbox Mode: Safely execute code and run tests in isolated environments
  • Multiple Models: Support for various Qwen models (qwen3-coder-plus, qwen3-coder-turbo, etc.)
  • Flexible Approval Modes: Control tool execution with plan/default/auto-edit/yolo modes
  • MCP Protocol: Seamless integration with MCP-compatible AI assistants

Prerequisites

  • Node.js v16 or higher
  • Qwen CLI installed and configured (qwen-code)

Installation

Quick Setup (Easiest - Recommended)

Use Claude Code's built-in MCP installer:

claude mcp add qwen-cli -- npx -y @jaggerxtrm/qwen-mcp-tool

This single command configures everything automatically!

Via Global Install

Install via npm:

npm install -g @jaggerxtrm/qwen-mcp-tool

Then add to Claude Code MCP settings (~/.config/claude/mcp_settings.json):

{
  "mcpServers": {
    "qwen-cli": {
      "command": "qwen-mcp-tool"
    }
  }
}

Via npx (Manual Configuration)

Manually configure to use npx without installing:

{
  "mcpServers": {
    "qwen-cli": {
      "command": "npx",
      "args": ["-y", "@jaggerxtrm/qwen-mcp-tool"]
    }
  }
}

From Source (Development)

  1. Clone and install dependencies:
git clone <repo-url>
cd qwen-mcp-tool
npm install
  1. Build the project:
npm run build
  1. Link locally:
npm link

Available Tools

ask-qwen

The main tool for interacting with Qwen AI.

Parameters:

  • prompt (required): Your question or instruction
    • Use @filename to include a file's contents
    • Use @directory to include all files in a directory
  • model (optional): Model to use (qwen3-coder-plus, qwen3-coder-turbo, etc.)
  • sandbox (optional): Enable sandbox mode for safe code execution
  • approvalMode (optional): Control tool execution approval
    • plan: Analyze tool calls without executing
    • default: Prompt for approval (default behavior)
    • auto-edit: Auto-approve file edits
    • yolo: Auto-approve all tool calls
  • yolo (optional): Shortcut for approvalMode='yolo'
  • allFiles (optional): Include all files in current directory as context
  • debug (optional): Enable debug mode

Examples:

// Analyze a specific file
{
  "prompt": "@src/main.ts Explain what this code does"
}

// Analyze entire codebase
{
  "prompt": "@src/ Summarize the architecture of this codebase"
}

// Use specific model with sandbox
{
  "prompt": "Run the test suite and fix any failures",
  "model": "qwen3-coder-plus",
  "sandbox": true,
  "approvalMode": "auto-edit"
}

ping

Simple echo test to verify the connection.

Parameters:

  • prompt (optional): Message to echo (defaults to "Pong!")

Help

Display Qwen CLI help information.

Parameters: None

Configuration

The tool uses the following default models:

  • Primary: qwen3-coder-plus
  • Fallback: qwen3-coder-turbo (used if primary hits quota limits)

You can override these by specifying the model parameter in your requests.

Usage with Claude Code

Once installed as an MCP server, you can use it within Claude Code:

Ask Qwen to analyze the authentication system in @src/auth/

Claude will automatically use the ask-qwen tool with the appropriate parameters.

Project Structure

qwen-mcp-tool/
├── src/
│   ├── index.ts              # MCP server entry point
│   ├── constants.ts          # Configuration and constants
│   ├── tools/
│   │   ├── registry.ts       # Tool registration system
│   │   ├── ask-qwen.tool.ts  # Main Qwen interaction tool
│   │   ├── simple-tools.ts   # Utility tools (ping, help)
│   │   └── index.ts          # Tool exports
│   └── utils/
│       ├── commandExecutor.ts # Command execution utility
│       ├── qwenExecutor.ts    # Qwen CLI wrapper
│       └── logger.ts          # Logging utility
├── package.json
├── tsconfig.json
└── README.md

How It Works

  1. The MCP server listens for tool calls via stdio transport
  2. When a tool is called, the server validates the arguments using Zod schemas
  3. For ask-qwen, the prompt is passed to the Qwen CLI with appropriate flags
  4. File references (@filename) are handled by Qwen's built-in file processing
  5. Output is captured and returned to the MCP client
  6. If quota limits are hit, the server automatically falls back to the turbo model

Comparison with Gemini MCP Tool

This tool is inspired by gemini-mcp-tool but adapted for Qwen CLI:

FeatureGemini MCPQwen MCP
File references✅ (more advanced)
Sandbox mode
Multiple models
Approval modes
Directory traversalBasicAdvanced (git-aware)
Multimodal supportLimitedImages, PDFs, audio, video

Troubleshooting

"Qwen CLI not found"

Make sure the Qwen CLI is installed and available in your PATH:

npm install -g @qwen/cli
# or follow instructions at https://github.com/QwenLM/qwen-code

"Command timed out"

For very large files or codebases, the analysis may take longer than the default 10-minute timeout. Consider:

  • Using .qwenignore to exclude unnecessary files
  • Breaking down large queries into smaller chunks
  • Using approvalMode: "plan" to analyze without executing

"Invalid tool arguments"

Check that your arguments match the tool schema. Use the Help tool to see available options.

License

MIT

Contributing

Contributions are welcome! Please feel free to submit issues or pull requests.

Credits

Inspired by gemini-mcp-tool by jamubc. Built for use with Qwen Code.

FAQ

What is the Qwen Code MCP server?
Qwen Code is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Qwen Code?
This profile displays 70 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.

Use Cases

Extended AI Capabilities

Add new capabilities to Claude beyond text generation

Example

Access external data sources, execute code, interact with tools and services

Transform Claude from chatbot to action-taking agent

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

Get more accurate, context-aware responses

Workflow Automation

Automate multi-step workflows combining AI and external tools

Example

Research → Summarize → Create document → Send notification

Complete complex tasks end-to-end without manual steps

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • Basic understanding of MCP architecture and capabilities
  • Access credentials for integrated services (if required)
  • Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • MCP server not loading: Check config syntax, verify installation
  • Connection errors: Check network, firewall, credentials
  • Feature not working: Read server docs, check required parameters
  • Performance issues: Monitor resource usage, check for network latency
  • Conflicts with other servers: Check port assignments, namespace collisions

Best Practices

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • Don't grant overly permissive access to MCP servers
  • Don't skip reading security considerations in docs
  • Don't expose sensitive data without proper controls
  • Don't run untrusted MCP servers without code review
  • Don't ignore error messages—investigate root cause

💡 Pro Tips

  • Combine multiple MCP servers for powerful workflows
  • Create custom MCP servers for your specific needs
  • Share successful configurations with team
  • Use MCP inspector for debugging
  • Join MCP community for tips and troubleshooting

Technical Details

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This

✓ Use When

Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.

✗ Avoid When

Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.

Integration

  • Tool composition: Chain multiple MCP tools in workflows
  • Context augmentation: Provide AI with relevant external data
  • Action delegation: Let AI execute tasks on external systems
  • Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.570 reviews
  • Chinedu Haddad· Dec 28, 2024

    Strong directory entry: Qwen Code surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Hiroshi Sanchez· Dec 28, 2024

    Qwen Code has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Arjun Johnson· Dec 16, 2024

    Qwen Code reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Chinedu Lopez· Dec 16, 2024

    Qwen Code is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Chaitanya Patil· Dec 8, 2024

    Qwen Code reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Arjun Taylor· Dec 8, 2024

    According to our notes, Qwen Code benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Chinedu Khanna· Dec 4, 2024

    Qwen Code has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Piyush G· Nov 27, 2024

    I recommend Qwen Code for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Hiroshi Ndlovu· Nov 27, 2024

    We evaluated Qwen Code against two servers with overlapping tools; this profile had the clearer scope statement.

  • Arjun Robinson· Nov 27, 2024

    We wired Qwen Code into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

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