Development · MODEL CONTEXT PROTOCOL

SDGLBL mcp claude code

MCP implementation of Claude Code capabilities and more

连接能力

SDGLBL mcp claude code 为 AI Agent 提供什么

  • **Code Understanding**: Analyze and understand codebases through file access and pattern searching
  • **Code Modification**: Make targeted edits to files with proper permission handling
  • **Enhanced Command Execution**: Run commands and scripts in various languages with improved error handling and shell support
  • **File Operations**: Manage files with proper security controls through shell commands
  • **Code Discovery**: Find relevant files and code patterns across your project with high-performance searching
  • **Agent Delegation**: Delegate complex tasks to specialized sub-agents that can work concurrently
  • **Multiple LLM Provider Support**: Configure any LiteLLM-compatible model for agent operations
  • **Jupyter Notebook Support**: Read and edit Jupyter notebooks with full cell and output handling
  • Permission prompts for file modifications and command execution
  • Restricted access to specified directories only
  • Input validation and sanitization
  • Proper error handling and reporting
  • Fork the repository
  • Create a feature branch (`git checkout -b feature/amazing-feature`)
  • Commit your changes (`git commit -m 'Add some amazing feature'`)
  • Push to the branch (`git push origin feature/amazing-feature`)
  • Open a Pull Request

安全

MCP 收录不等于安全审核

MCP 服务器可能获得模型上下文、凭据、本地文件或调用外部系统的权限。连接 Agent 前,请检查代码、环境变量、网络行为、软件包来源与维护状态。

README

仓库文档

MCP Claude Code

An implementation of Claude Code capabilities using the Model Context Protocol (MCP).

Overview

This project provides an MCP server that implements Claude Code-like functionality, allowing Claude to directly execute instructions for modifying and improving project files. By leveraging the Model Context Protocol, this implementation enables seamless integration with various MCP clients including Claude Desktop.

example

Features

  • Code Understanding: Analyze and understand codebases through file access and pattern searching
  • Code Modification: Make targeted edits to files with proper permission handling
  • Enhanced Command Execution: Run commands and scripts in various languages with improved error handling and shell support
  • File Operations: Manage files with proper security controls through shell commands
  • Code Discovery: Find relevant files and code patterns across your project with high-performance searching
  • Agent Delegation: Delegate complex tasks to specialized sub-agents that can work concurrently
  • Multiple LLM Provider Support: Configure any LiteLLM-compatible model for agent operations
  • Jupyter Notebook Support: Read and edit Jupyter notebooks with full cell and output handling

Tools Implemented

Tool Description
read Read file contents with line numbers, offset, and limit capabilities
write Create or overwrite files
edit Make line-based edits to text files
multi_edit Make multiple precise text replacements in a single file operation with atomic transactions
directory_tree Get a recursive tree view of directories
grep Fast pattern search in files with ripgrep integration for best performance (docs)
content_replace Replace patterns in file contents
grep_ast Search code with AST context showing matches within functions, classes, and other structures
run_command Execute shell commands (also used for directory creation, file moving, and directory listing)
notebook_read Extract and read source code from all cells in a Jupyter notebook with outputs
notebook_edit Edit, insert, or delete cells in a Jupyter notebook
think Structured space for complex reasoning and analysis without making changes
dispatch_agent Launch one or more agents that can perform tasks using read-only tools concurrently
batch Execute multiple tool invocations in parallel or serially in a single request
todo_write Create and manage a structured task list
todo_read Read a structured task list

Getting Started

For detailed installation and configuration instructions, please refer to INSTALL.md.

For detailed tutorial of 0.3 version, please refer to TUTORIAL.md

Security

This implementation follows best practices for securing access to your filesystem:

  • Permission prompts for file modifications and command execution
  • Restricted access to specified directories only
  • Input validation and sanitization
  • Proper error handling and reporting

Development

To contribute to this project:

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add some amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

在 GitHub 查看 README

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