Qwen Code▌
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.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
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
@filenameor@directorysyntax 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)
- Clone and install dependencies:
git clone <repo-url>
cd qwen-mcp-tool
npm install
- Build the project:
npm run build
- Link locally:
npm link
Available Tools
ask-qwen
The main tool for interacting with Qwen AI.
Parameters:
prompt(required): Your question or instruction- Use
@filenameto include a file's contents - Use
@directoryto include all files in a directory
- Use
model(optional): Model to use (qwen3-coder-plus, qwen3-coder-turbo, etc.)sandbox(optional): Enable sandbox mode for safe code executionapprovalMode(optional): Control tool execution approvalplan: Analyze tool calls without executingdefault: Prompt for approval (default behavior)auto-edit: Auto-approve file editsyolo: Auto-approve all tool calls
yolo(optional): Shortcut for approvalMode='yolo'allFiles(optional): Include all files in current directory as contextdebug(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
- The MCP server listens for tool calls via stdio transport
- When a tool is called, the server validates the arguments using Zod schemas
- For
ask-qwen, the prompt is passed to the Qwen CLI with appropriate flags - File references (
@filename) are handled by Qwen's built-in file processing - Output is captured and returned to the MCP client
- 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:
| Feature | Gemini MCP | Qwen MCP |
|---|---|---|
| File references | ✅ | ✅ (more advanced) |
| Sandbox mode | ✅ | ✅ |
| Multiple models | ✅ | ✅ |
| Approval modes | ❌ | ✅ |
| Directory traversal | Basic | Advanced (git-aware) |
| Multimodal support | Limited | Images, 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
.qwenignoreto 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.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 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
4.5★★★★★70 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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