mcp4MCP (Hmatt1)

mcp4mcp

Meta MCP服务器 -AI驱动的开发助手，用于构建更好的MCP项目

  

mcp4mcp 自动跟踪您的MCP工具，检测重复项，提出改进建议，并在整个开发过程中提供人工智能指导。

⚡ 零安装设置（推荐）

喜欢 npx 对于Node.js，请使用 uvx 对于即时Python包执行：

{
  "mcpServers": {
    "mcp4mcp": {
      "command": "uvx",
      "args": ["--from", "mcp4mcp", "mcp4mcp-server"]
    }
  }
}

无需安装！ 需要时，Claude Desktop会自动下载并运行mcp4mcp。

🚀 快速开始

安装

# Install mcp4mcp from PyPI
pip install mcp4mcp

使用Claude Desktop进行设置

将此添加到您的Claude桌面 claude_desktop_config.json 文件：

{
  "mcpServers": {
    "mcp4mcp": {
      "command": "mcp4mcp-server",
      "args": []
    }
  }
}

Claude Desktop将自动启动和管理mcp4mcp服务器。那就聊聊吧：

*“帮助我构建一个用于文件处理的MCP服务器。使用mcp4mcp跟踪进度并避免重复。”*

手动使用（可选）

# Or run manually for testing
mcp4mcp-server

# Or run the demo
mcp4mcp demo

开发设置

对于本地开发和测试：

# Clone and install from source
git clone https://github.com/hmatt1/mcp4mcp.git
cd mcp4mcp
pip install -e .

使用Claude Desktop进行测试（开发）：

{
  "mcpServers": {
    "mcp4mcp-dev": {
      "command": "python",
      "args": ["C:\\path\\to\\your\\mcp4mcp\\server.py"]
    }
  }
}

替换 C:\\path\\to\\your\\mcp4mcp\\ 根据您的实际项目路径。

故障排除：

• 确保依赖关系： pip install fastmcp pydantic aiosqlite
• 测试导入： python -c "import mcp4mcp; print('OK')"
• 测试服务器： python server.py

与Claude Desktop一起使用

mcp4mcp 与无缝集成 克劳德桌面版 对于人工智能驱动的MCP开发：

# 1. Start mcp4mcp server (in one terminal)
mcp4mcp-server

# 2. Configure Claude Desktop MCP settings
# Add to your Claude Desktop configuration:
# Server Name: mcp4mcp
# Command: mcp4mcp-server

# 3. Chat with Claude Desktop about MCP development
"Help me build an MCP server for database operations. Use mcp4mcp to track progress."

Claude Desktop可以用mcp4mcp做什么：

• 🎯 跟踪发展会议 自动地
• 🔍 检查是否有重复的工具 建造前
• 💡 提供AI建议 基于您的项目状态
• 📊 监督进展 跨开发会议
• 🔄 发现现有工具 在你的代码库中

开发安装

# Install from source for development
git clone https://github.com/hmatt1/mcp4mcp.git
cd mcp4mcp && pip install -e .
python server.py

💡 所得

• 🧠 AI建议 下一步开发步骤
• 🔍 重复检测 在构建冲突的工具之前
• 📊 进度跟踪 跨开发会议
• 🔄 自动发现 代码库中的工具
• 📈 分析 关于你的发展模式

🛠️ 核心工具

🤖 与Claude Desktop一起使用

mcp4mcp 设计用于完美配合 克劳德桌面版 用于智能MCP开发：

快速设置

# Terminal 1: Start mcp4mcp server
mcp4mcp-server

# Terminal 2: Configure Claude Desktop MCP settings
# In Claude Desktop:
# - Go to Settings → MCP Servers
# - Add new server:
#   Name: mcp4mcp
#   Command: mcp4mcp-server
#   Args: (leave empty)

克劳德桌面会话示例

构建新的MCP服务器

You: "Help me build an MCP server for file processing with CSV and JSON support."

Claude Desktop automatically:
1. 🎯 Calls track_development_session_tool("file-processing-mcp", "Building CSV/JSON tools")
2. 🔍 Uses check_before_build_tool to see if similar tools exist
3. 💡 Calls suggest_next_action_tool for personalized guidance
4. 📊 Tracks progress as you build each tool

避免重复工作

You: "I want to add a data validation tool to my project."

Claude Desktop automatically:
1. 🔍 Scans existing tools with scan_project_files_tool
2. ⚠️  Alerts if similar validation tools already exist
3. 💡 Suggests reusing or extending existing tools instead
4. 🎯 Tracks the decision in your development session

获取发展见解

You: "What should I work on next for my MCP project?"

Claude Desktop automatically:
1. 📊 Calls get_session_analytics_tool for project insights
2. 💡 Uses suggest_next_action_tool based on current state
3. 🎯 Provides personalized recommendations
4. 📈 Shows development patterns and progress

克劳德桌面+mcp4mcp的优点

• 🧠 智能上下文：Claude Desktop了解MCP开发模式
• 🔍 自动冲突检测：防止重复的工具开发
• 📊 连续跟踪：每个开发操作都会自动记录
• 💡 上下文建议：基于您特定项目状态的AI指导
• 🚀 加速发展：专注于建筑，而不是项目管理

📋 使用示例

开始开发会议

# Log what you're working on
await track_development_session(
    "Building file processing tools", 
    "my_project",
    "csv_reader"
)

建造前检查

# Avoid duplicates
result = await check_before_build(
    "file_processor", 
    "Process CSV files",
    "my_project" 
)

if result['conflicts']:
    print("⚠️ Similar tools exist - consider reusing instead")

获取AI建议

# Get personalized guidance
suggestions = await suggest_next_action(
    "my_project",
    "Just finished the CSV reader, what's next?"
)

for suggestion in suggestions['suggestions']:
    print(f"💡 {suggestion}")

自动发现工具

# Scan your codebase
result = await scan_project_files("my_project", "./src")
print(f"🔍 Found {result['new_tools']} new tools")

🤖 人工智能驱动的MCP开发

将此提示模板与任何LLM一起使用，以构建利用mcp4mcp的MCP服务器：

# MCP Server Development with mcp4mcp

You are an expert MCP (Model Context Protocol) developer building a new MCP server. 
You have access to mcp4mcp tools that provide intelligent development assistance.

## Your Development Process:

1. **Start Each Session**: Always begin by calling `track_development_session_tool` 
   to log what you're working on

2. **Before Building Any Tool**: Call `check_before_build_tool` to check for 
   conflicts and similar existing tools

3. **Get AI Guidance**: Use `suggest_next_action_tool` for personalized 
   development recommendations based on project state

4. **Update Progress**: Use `update_project_state_tool` to track tools as you 
   build them (planned → in_progress → completed)

5. **Discover Existing Tools**: Use `scan_project_files_tool` to automatically 
   find tools in the codebase

6. **Check for Duplicates**: Run `analyze_tool_similarity_tool` periodically 
   to find similar tools that could be consolidated

## Current Task:
Build a [DOMAIN] MCP server with tools for [SPECIFIC_FUNCTIONALITY].

## Project Details:
- Project name: [PROJECT_NAME]
- Description: [PROJECT_DESCRIPTION] 
- Key requirements: [LIST_REQUIREMENTS]

Start by calling the appropriate mcp4mcp tools to understand the current state 
and get AI-powered suggestions for the best approach.

提示用法示例：

# MCP Server Development with mcp4mcp

You are an expert MCP developer building a new MCP server. You have access to mcp4mcp tools.

## Current Task:
Build a file processing MCP server with tools for reading, writing, and transforming CSV/JSON files.

## Project Details:
- Project name: file-processor-mcp
- Description: MCP server for file operations with data transformation capabilities
- Key requirements: 
  * Read CSV and JSON files
  * Write data in multiple formats  
  * Transform data between formats
  * Validate file schemas
  * Handle large files efficiently

Start by calling mcp4mcp tools to check current state and get development guidance.

示例提示：文件系统MCP服务器

# MCP Server Development with mcp4mcp

You are an expert MCP (Model Context Protocol) developer building a new MCP server.
You have access to mcp4mcp tools that provide intelligent development assistance.

## Current Task:
Build a file system MCP server with comprehensive file and directory management capabilities.

## Project Details:
- Project name: filesystem-mcp
- Description: A robust MCP server providing file system operations with safety limits and encoding support
- Key requirements:
  * List files and directories with recursive options and safety limits
  * Read file contents with multiple encoding support (utf-8, binary, etc.)
  * Support reading files in chunks and with size limits
  * Generate hex dumps of binary files
  * Search files by extension, regex patterns, and other criteria
  * Create new files safely with proper error handling
  * Make line replacements in text files with backup options
  * Proper error handling and validation for all operations
  * Support for common character encodings
  * Built-in safety limits to prevent abuse
  
## Implementation Guidelines:
1. Start with core file listing and reading capabilities
2. Add extension filtering and search functionality
3. Implement file creation and modification features
4. Add safety checks and validation throughout
5. Support multiple encoding formats
6. Implement chunked reading for large files
7. Add hex dump capability for binary files

## Architecture Considerations:
- Use a modular design to separate different file operations
- Implement proper validation and safety checks
- Consider adding configuration options for limits and defaults
- Include comprehensive error handling
- Add logging for file operations

## How to Use mcp4mcp Tools for This Project

To ensure a robust, efficient, and non-duplicative development process, rely on mcp4mcp tools at every stage:

1. **Track Your Development Sessions**  
   Use `track_development_session_tool` to log each new session, including your current focus (e.g., "Implementing file listing"). This creates a clear project history and helps with analytics.

2. **Check for Existing Tools Before Building**  
   Before implementing any new feature (such as file reading or extension search), always call `check_before_build_tool` to detect similar or duplicate tools in your project. This prevents wasted effort and encourages code reuse.

3. **Get AI Guidance for Each Step**  
   Use `suggest_next_action_tool` to get personalized, context-aware recommendations for your next development steps. This leverages mcp4mcp's AI to optimize your workflow and avoid common pitfalls.

4. **Scan the Project for Existing Code**  
   Regularly run `scan_project_files_tool` to auto-discover tools and code patterns in your codebase. This helps you find reusable components and ensures your project state is always up to date.

5. **Update Project State as You Build**  
   As you add or modify tools, use `update_project_state_tool` to keep your project metadata current. Mark tools as planned, in progress, or completed for accurate tracking.

6. **Analyze for Similarity and Duplicates**  
   Periodically run `analyze_tool_similarity_tool` to identify overlapping or redundant tools, so you can consolidate functionality and keep your codebase clean.

7. **Review Analytics and Progress**  
   Use `get_session_analytics_tool` to review your development patterns, session history, and tool usage. This helps you reflect on your workflow and plan improvements.

By integrating these mcp4mcp tools into your workflow, you ensure that your file system MCP server is built efficiently, avoids duplication, and benefits from continuous AI-powered guidance and tracking.

文件系统MCP服务器的基本使用示例：

# Example tool invocations once built
files = await list_files_tool(
    path="./src",
    recursive=True,
    max_depth=3,
    exclude_patterns=["*.pyc", "__pycache__"]
)

content = await read_file_tool(
    path="./config.json",
    encoding="utf-8",
    max_size_mb=10
)

search_results = await find_files_tool(
    pattern="*.py",
    search_dir="./src",
    recursive=True
)

await replace_in_file_tool(
    path="./settings.ini",
    old_string="debug=false",
    new_string="debug=true",
    backup=True
)

🔧 整合

将mcp4mcp添加到任何FastMCP项目中：

from fastmcp import FastMCP
from mcp4mcp.tools.state_management import register_state_tools
from mcp4mcp.tools.intelligence import register_intelligence_tools  
from mcp4mcp.tools.tracking import register_tracking_tools

# Your MCP server
mcp = FastMCP("your-server")

# Add mcp4mcp intelligence
register_state_tools(mcp)
register_intelligence_tools(mcp) 
register_tracking_tools(mcp)

# Your tools
@mcp.tool()
def your_tool():
    return "Hello World"

mcp.run()

📊 发展分析

查看您的开发模式：

# Get insights on your development
analytics = await get_session_analytics("my_project", days=7)

print(f"📈 This week:")
print(f"  Sessions: {analytics['total_sessions']}")  
print(f"  Time: {analytics['total_development_time']}")
print(f"  Tools: {len(analytics['tools_worked_on'])}")

🗃️ 数据存储

所有数据都存储在本地 ~/.mcp4mcp/projects.db -没有外部依赖。

🧪 测试

# Run demo
python main.py demo

# Run tests  
python main.py test

# FastMCP diagnostics
python run_diagnostic.py

🛠️ 发展

# Setup
git clone https://github.com/hmatt1/mcp4mcp.git
cd mcp4mcp
pip install -e ".[dev]"

# Test
python -m pytest tests/ -v

📄 许可证

这是免费且无阻碍的软件，已发布到公共领域。请参阅 未授权 文件以获取详细信息。

🤝 支持

• 🐛 问题:
• 💬 讨论:
• 📚 例子：参见 examples/ 目录

______________________________________________________________________

mcp4mcp -MCP开发的智能🧠✨