AI Skill Hub 推荐使用:MCP调试器 是一款优质的MCP工具。AI 综合评分 7.5 分,在同类工具中表现稳健。如果你正在寻找可靠的MCP工具解决方案,这是一个值得深入了解的选择。
AI驱动的调试服务器,赋予AI代理一步步调试超能力
MCP调试器 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
AI驱动的调试服务器,赋予AI代理一步步调试超能力
MCP调试器 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
# 方式一:通过 Claude Code CLI 一键安装
claude skill install https://github.com/debugmcp/mcp-debugger
# 方式二:手动配置 claude_desktop_config.json
{
"mcpServers": {
"mcp---": {
"command": "npx",
"args": ["-y", "mcp-debugger"]
}
}
}
# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
# 安装后在 Claude 对话中直接使用 # 示例: 用户: 请帮我用 MCP调试器 执行以下任务... Claude: [自动调用 MCP调试器 MCP 工具处理请求] # 查看可用工具列表 # 在 Claude 中输入:"列出所有可用的 MCP 工具"
// claude_desktop_config.json 配置示例
{
"mcpServers": {
"mcp___": {
"command": "npx",
"args": ["-y", "mcp-debugger"],
"env": {
// "API_KEY": "your-api-key-here"
}
}
}
}
// 保存后重启 Claude Desktop 生效
MCP server for multi-language debugging – give your AI agents debugging superpowers 🚀
mcp-debugger is a Model Context Protocol (MCP) server that provides debugging tools as structured API calls. It enables AI agents to perform step-through debugging of multiple programming languages using the Debug Adapter Protocol (DAP).
🆕 Version 0.19.0: Java debugging via JDI bridge with launch and attach modes! Plus Go debugging with Delve.
🆕 Version 0.17.0: Rust debugging support! Debug Rust programs with CodeLLDB on Linux/macOS, including Cargo projects, async code, and full variable inspection—plus step commands now return the active source context so agents keep their place automatically.
🔥 Version 0.16.0: JavaScript/Node.js debugging support! Full debugging capabilities with bundled js-debug, TypeScript support, and zero-runtime dependencies via improved npx distribution.
🎬 Demo Video: See the debugger in action! Recording in progress - This will show an AI agent discovering and fixing the variable swap bug in real-time
- 🌐 Multi-language support – Clean adapter pattern for any language - 🐍 Python debugging via debugpy – Full DAP protocol support - 🟨 JavaScript (Node.js) debugging via js-debug – VSCode's proven debugger - 🦀 Rust debugging via CodeLLDB – Debug Rust & Cargo projects (Linux/macOS/Windows with GNU toolchain) - 🐹 Go debugging via Delve – Full DAP support for Go programs - ☕ Java debugging via JDI bridge – Launch and attach modes with JDK 21+ - 🔷 .NET/C# debugging via netcoredbg – Debug .NET applications with full DAP support > WARNING: On Windows, use the GNU toolchain for full variable inspection. Run mcp-debugger check-rust-binary <path-to-exe> to verify your build and see Rust Debugging on Windows for detailed guidance. > NOTE: The published npm bundle ships the Linux x64 CodeLLDB runtime to stay under registry size limits. On macOS or Windows, point the CODELLDB_PATH environment variable at an existing CodeLLDB installation (for example from the VSCode extension) or clone the repo and run pnpm --filter @debugmcp/adapter-rust run build:adapter to vendor your platform binaries locally.
pnpm install
If you're on Windows and want the quickest path to a working GNU toolchain + dlltool configuration, run:
pwsh scripts/setup/windows-rust-debug.ps1
The script installs the stable-gnu toolchain (via rustup), sets up dlltool.exe (preferring MSYS2/MinGW when available, falling back to rustup's self-contained copy), builds the bundled Rust examples, and runs the Rust smoke tests by default. Add -SkipTests to opt out of running tests. Add -UpdateUserPath if you want the dlltool path persisted to your user PATH/DLLTOOL variables.
The script will also attempt to provision an MSYS2-based MinGW-w64 toolchain (via winget + pacman) so cargo +stable-gnu has a fully functional dlltool/ld/as stack. If MSYS2 is already installed, it simply reuses it; otherwise it guides you through installing it (or warns so you can install manually). - 🧪 Mock adapter for testing – Test without external dependencies - 🔌 STDIO and Streamable HTTP transports – Works with any MCP client (legacy SSE transport is deprecated) - 📦 Zero-runtime dependencies – Self-contained bundles via esbuild + tsup - ⚡ npx ready – Run directly with npx @debugmcp/mcp-debugger - no installation needed - 📊 1266+ tests passing – battle-tested end-to-end - 🐳 Docker and npm packages – Deploy anywhere - 🤖 Built for AI agents – Structured JSON responses for easy parsing - 🛡️ Path validation – Prevents crashes from non-existent files - 📝 AI-aware line context – Intelligent breakpoint placement with code context
./scripts/install-claude-mcp.sh
docker run -v $(pwd):/workspace debugmcp/mcp-debugger:latest
⚠️ The Docker image ships Python, JavaScript, Go, Java, and .NET adapters. Rust debugging requires the local, SSE, or packed deployments where the adapter runs next to your toolchain. Note: adapters are loaded dynamically at runtime — only those whose toolchain is installed and detected will be reported as available by list_supported_languages.
git clone https://github.com/debugmcp/mcp-debugger.git cd mcp-debugger
pnpm build
docker build -t mcp-debugger:local .
Here's a complete debugging session example:
```python
高质量的MCP调试工具,具有很大的潜力
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。
总体来看,MCP调试器 是一款质量良好的MCP工具,在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态,建议收藏备用,结合自身场景选择合适时机引入使用。
| 原始名称 | mcp-debugger |
| Topics | LLM调试器AI代理 |
| GitHub | https://github.com/debugmcp/mcp-debugger |
| License | MIT |
| 语言 | TypeScript |
收录时间:2026-05-26 · 更新时间:2026-05-26 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。
选择 Agent 类型,复制安装指令后粘贴到对应客户端