Content
# DataMaster MCP Server
## Project Introduction
**DataMaster MCP Server** is a powerful data analysis management server built on **Model Context Protocol (MCP)**. This project stems from several core pain points encountered in modern data analysis work:
- **Data source fragmentation**: Enterprise data is scattered across different systems such as databases, APIs, and files, making integration difficult.
- **Complex analysis process**: Switching between multiple tools is required from data acquisition to analysis results, resulting in low efficiency.
- **Difficult collaboration**: Lack of a unified interaction interface between data analysts, developers, and business personnel.
- **Repetitive labor**: Repeatedly writing code for data connection, cleaning, and transformation for each analysis.
To solve these problems, we developed this **one-stop data management and analysis platform** that integrates database operations, API integration, data processing, and analysis calculations into a unified MCP server. Users can complete complex data analysis tasks with simple natural language instructions.
### Core Features
🔍 **Multi-Data Source Integration**
- Supports mainstream databases such as SQLite, MySQL, and PostgreSQL
- Integrates RESTful API data acquisition
- Unified data source management interface
📊 **Intelligent Data Processing**
- Data cleaning (deduplication, missing value handling, type conversion)
- Data transformation (column renaming, standardization, calculating new columns)
- Data filtering (conditional filtering, sampling, column selection)
📈 **Advanced Analysis Capabilities**
- Descriptive statistical analysis
- Trend analysis and prediction
- Data visualization preview
💾 **Flexible Data Export**
- Supports multiple formats such as Excel, CSV, and JSON
- Automatically generates standardized reports
- One-click sharing of analysis results
## Deployment Guide
### Environment Requirements
- **Python**: 3.8 or higher
- **Operating System**: Windows 10/11, macOS 10.15+, Ubuntu 18.04+
- **Memory**: Minimum 4GB RAM (8GB or more recommended)
- **Storage**: At least 1GB of available space
#### 2. Install Dependencies
```bash
pip install datamaster-mcp
```
#### 3. Configuration File Settings
Create a configuration file `config/mcp_config.json`:
```json
{
"mcpServers": {
"datamaster-mcp": {
"command": "uvx",
"args": [
"datamaster-mcp"
]
}
}
}
```
## Usage Examples
### Example 1: Connect to Database and Query Data
**User Input:**
```
连接到本地的sales数据库，查询2024年的销售数据
```
**System Response:**
```
✅ 数据库连接成功！
📊 查询结果：找到15,847条2024年销售记录
数据概览：
├─ 总销售额：¥2,847,392.50
├─ 订单数量：15,847笔
├─ 平均订单金额：¥179.68
└─ 数据时间范围：2024-01-01 至 2024-12-31
数据已保存到表：sales_2024_analysis
```
### Example 2: API Data Acquisition and Analysis
**User Input:**
```
从天气API获取北京最近7天的天气数据，分析温度趋势
```
**System Response:**
```
🌤️ 天气数据获取成功！
📈 温度趋势分析完成
7天温度统计：
├─ 最高温度：28.5°C (10月15日)
├─ 最低温度：12.3°C (10月12日)
├─ 平均温度：20.8°C
└─ 温度变化：整体呈下降趋势 (-3.2°C)
📊 数据可视化已生成：exports/weather_trend_20241015_143022.png
```
### Example 3: Data Cleaning and Export
**User Input:**
```
清洗用户数据表，去除重复邮箱，填充缺失的手机号码
```
**System Response:**
```
🧹 数据清洗完成！
清洗结果统计：
├─ 原始记录：8,432条
├─ 重复邮箱：127条（已删除）
├─ 缺失手机：2,156条（已填充默认值）
├─ 数据类型转换：3列
└─ 清洗后记录：8,305条
📤 清洗结果已导出：exports/cleaned_users_20241015_144512.xlsx
```
### Example 4: Complex SQL Query and Visualization
**User Input:**
```
查询每个产品类别的月度销售额，按销售额降序排列
```
**System Response:**
```
📊 查询执行成功！
月度销售排行榜：
├─ 电子产品：¥847,392.00 (占比29.7%)
├─ 服装配饰：¥624,193.50 (占比21.9%)
├─ 家居用品：¥518,472.30 (占比18.2%)
├─ 图书文具：¥392,847.20 (占比13.8%)
└─ 运动户外：¥347,392.00 (占比12.2%)
📈 可视化图表已生成：
├─ 柱状图：exports/category_sales_bar_20241015_145533.png
├─ 饼图：exports/category_sales_pie_20241015_145533.png
└─ 数据表：exports/category_monthly_sales.xlsx
```
### Actual Usage Screenshot Display
#### Main Interface Display
```
┌─────────────────────────────────────────────────────────────┐
│ DataMaster MCP Server │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ 🔍 数据源管理 📊 数据分析 ⚙️ 数据处理 🌐 API │ │
│ └─────────────────────────────────────────────────────┘ │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ 已连接数据源: │ │
│ │ 📊 SQLite数据库: analysis.db (已连接) │ │
│ │ 🌐 天气API: api.weather.com (已配置) │ │
│ │ 📈 股票API: api.stocks.com (已配置) │ │
│ └─────────────────────────────────────────────────────┘ │
│ [输入指令] > 连接到sales数据库，查询2024年销售数据... │
└─────────────────────────────────────────────────────────────┘
```
#### Database Connection Configuration
```
┌─────────────────────────────────────────┐
│ 数据库连接配置 │
├─────────────────────────────────────────┤
│ 数据库类型: SQLite │
│ 数据库路径: ./data/sales.db │
│ 连接状态: ✅ 已连接 │
│ 表数量: 12张 │
│ 总记录数: 125,847条 │
├─────────────────────────────────────────┤
│ 可用表: │
│ • customers (8,432条) │
│ • orders (15,847条) │
│ • products (342条) │
│ • sales_records (45,293条) │
└─────────────────────────────────────────┘
```
#### Data Query Results
```
┌─────────────────────────────────────────────┐
│ SQL查询结果 │
├─────────────────────────────────────────────┤
│ 查询: SELECT * FROM sales WHERE year=2024 │
│ 执行时间: 0.23秒 │
│ 返回记录: 15,847条 │
├─────────────────────────────────────────────┤
│ 数据概览: │
│ ├─ 总销售额: ¥2,847,392.50 │
│ ├─ 平均订单: ¥179.68 │
│ ├─ 最高单笔: ¥12,450.00 │
│ └─ 数据范围: 2024-01-01 ~ 2024-12-31 │
└─────────────────────────────────────────────┘
```
#### API Data Acquisition
```
┌─────────────────────────────────────────────┐
│ API数据获取 │
├─────────────────────────────────────────────┤
│ API名称: 天气数据API │
│ 请求URL: api.weather.com/v1/forecast │
│ 响应状态: ✅ 200 OK │
│ 数据大小: 45.2 KB │
├─────────────────────────────────────────────┤
│ 数据预览: │
│ 日期 温度 湿度 天气 │
│ 2024-10-15 28.5°C 65% 晴 │
│ 2024-10-14 26.2°C 70% 多云 │
│ 2024-10-13 24.8°C 72% 阴 │
└─────────────────────────────────────────────┘
```
#### Data Cleaning Tools
```
┌─────────────────────────────────────────────┐
│ 数据清洗结果 │
├─────────────────────────────────────────────┤
│ 清洗前记录: 8,432条 │
│ 清洗后记录: 8,305条 │
│ 删除重复: 127条 │
│ 填充缺失: 2,156条 │
├─────────────────────────────────────────────┤
│ 清洗操作: │
│ ✓ 去除重复邮箱地址 │
│ ✓ 填充缺失手机号码 │
│ ✓ 标准化日期格式 │
│ ✓ 验证邮箱格式 │
└─────────────────────────────────────────────┘
```
#### Analysis Result Export
```
┌─────────────────────────────────────────────┐
│ 导出结果 │
├─────────────────────────────────────────────┤
│ 导出格式: Excel (.xlsx) │
│ 文件路径: exports/sales_analysis.xlsx │
│ 文件大小: 2.34 MB │
│ 包含工作表: 3个 │
├─────────────────────────────────────────────┤
│ 工作表内容: │
│ • 销售概览 │
│ • 月度趋势 │
│ • 产品分析 │
└─────────────────────────────────────────────┘
```
### Advanced Usage Tips
#### Batch Processing
```bash
# 批量处理多个数据表
python -m datamaster_mcp.main --batch-process tables.txt --config batch_config.json
```
#### Scheduled Tasks
```bash
# 设置定时数据更新
python -m datamaster_mcp.main --schedule "0 9 * * *" --task daily_report
```
#### Custom Analysis Templates
```json
{
"template_name": "monthly_sales_analysis",
"steps": [
{"action": "query", "sql": "SELECT * FROM sales WHERE date >= ?"},
{"action": "clean", "config": {"remove_duplicates": true}},
{"action": "analyze", "type": "trend"},
{"action": "export", "format": "excel"}
]
}
```
## Troubleshooting
### Common Issues
**Q: Failed to connect to the database**
A: Check the database configuration and network connection to ensure the database service is running
**Q: API request timed out**
A: Increase the API timeout setting and check the stability of the network connection
**Q: Out of memory error**
A: Reduce the amount of data processed at one time, or increase system memory
### Log Viewing
```bash
# 查看实时日志
tail -f logs/datamaster.log
# 查看错误日志
grep ERROR logs/datamaster.log
```
## Technical Support
- **GitHub Issues**: [Submit Issue](https://github.com/your-username/DataMaster-MCP/issues)
- **Documentation**: [Complete Documentation](https://datamaster-mcp.readthedocs.io)
- **Community**: [Discussion Forum](https://github.com/your-username/DataMaster-MCP/discussions)
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**DataMaster MCP Server** - Making Data Analysis Simple and Powerful!
