# 📊 DataMaster MCP
> **超级数据分析MCP工具** - 为AI提供强大的数据分析能力
## 🎯 核心理念
**工具专注数据获取和计算，AI专注智能分析和洞察**
## 🚀 快速开始
### 用户安装
```bash
pip install datamaster-mcp
```
### 开发者安装
```bash
# 1. 克隆项目
git clone https://github.com/szqshan/DataMaster.git
cd DataMaster
# 2. 自动设置开发环境
python scripts/setup_dev.py
# 3. 测试环境
python scripts/setup_dev.py --test-only
```
### 基本使用
1. **配置 Claude Desktop**
将以下配置添加到 Claude Desktop 的配置文件中：
```json
{
"mcpServers": {
"datamaster-mcp": {
"command": "python",
"args": ["-m", "datamaster_mcp.main"],
"env": {
"PYTHONPATH": "/path/to/your/project"
}
}
}
}
```
2. **使用示例**
```python
# 导入Excel数据
connect_data_source(
source_type="excel",
config={"file_path": "data.xlsx"},
target_table="my_data"
)
# 执行SQL查询
execute_sql("SELECT * FROM my_data LIMIT 10")
# 数据分析
analyze_data(analysis_type="basic_stats", table_name="my_data")
# 导出结果
export_data(export_type="excel", data_source="my_data")
```
## ✨ 核心功能
### 📁 数据导入导出
- **Excel/CSV文件导入** - 支持多种格式和编码
- **数据库连接** - MySQL、PostgreSQL、MongoDB、SQLite
- **API数据获取** - RESTful API连接和数据提取
- **多格式导出** - Excel、CSV、JSON格式导出
### 🔍 数据查询分析
- **SQL查询执行** - 本地和外部数据库查询
- **数据统计分析** - 基础统计、相关性、异常值检测
- **数据质量检查** - 缺失值、重复值分析
### 🛠️ 数据处理
- **数据清洗** - 去重、填充缺失值
- **数据转换** - 类型转换、格式化
- **数据聚合** - 分组统计、汇总
## 📚 文档
### 用户文档
- [用户使用手册](用户使用手册.md)
- [本地测试指南](LOCAL_TEST_GUIDE.md)
### 开发者文档
- [开发者文档](开发者文档.md)
- [开发流程指南](DEVELOPMENT_WORKFLOW.md) 🆕
- [项目结构说明](项目结构说明.md)
- [PyPI发布指南](PYPI_RELEASE_GUIDE.md)
### 快速开发
```bash
# 设置开发环境
python scripts/setup_dev.py
# 运行测试
python scripts/setup_dev.py --test-only
# 发布新版本
python scripts/release.py 1.0.2
```
### 版本信息
- **[更新日志](CHANGELOG.md)** - 版本更新记录
- **[版本信息](VERSION.md)** - 当前版本详情
## 🛡️ 安全特性
- SQL注入防护
- 危险操作拦截
- 查询结果限制
- 参数验证
- 环境变量管理敏感信息
## 📞 支持
- 📖 查看[用户使用手册](用户使用手册.md)获取详细使用说明
- 🛠️ 查看[开发者文档](开发者文档.md)了解技术细节
- 📁 查看[项目结构说明](项目结构说明.md)了解文件组织
- 🐛 提交Issue报告问题或建议
---
**版本**: v1.0.0 | **状态**: ✅ 稳定版 | **更新**: 2025-01-24
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