Falahgs Gemini Data Analysis Research MCP Server

🚀 Venus Data Analysis Server

This project is a data processing server that leverages the Google Gemini model to offer efficient data analysis and task automation services. It provides a comprehensive set of tools for data analysis, automation, and content generation.

✨ Features

Data Analysis Tools

• File Parsing: Supports .xlsx, .xls, and .csv formats.
• Basic Analysis:
  • Descriptive statistics.
  • Data distribution analysis.
• In - depth Analysis:
  • Data modeling.
  • Predictive analysis.
• Visual Output:
  • Dynamic charts.
  • Report generation.

Automation Tools

• Task Scheduling.
• Workflow Management.
• Log Tracking.

Intelligent Generation Module

• Content generation based on the Gemini model.
• Multi - language support.
• Text summarization and translation functions.

🚀 Quick Start

Installation Steps

1. Clone the code repository.
2. Install dependencies: npm install.
3. Create a configuration file .env.

Environment Variable Settings

GEMINI_API_KEY=Your API key
NODEMAILER_EMAIL=Your email
NODEMAILER_PASSWORD=App password

💻 Usage Examples

Data Analysis Tool Interface

interface AnalyzeDataParams {
  fileData: string;         // Base64 file content
  fileName: string;         // File name (only .xlsx, .xls, .csv are supported)
  analysisType: 'basic' | 'detailed';  // Analysis type
  outputDir?: string;      // Optional output directory
}

Automation Tool Interface

interface AutomationParams {
  taskName: string;          // Task name
  parameters?: Record<string, any>;   // Additional parameters
}

📚 Documentation

Output Structure

/output/
├── reports/         # Analysis reports
│   ├── basic_analysis_time.html
│   └── detailed_analysis_time.html
├── logs/
│   ├── task_log_time.log
│   └── error_log_time.log
└── models/
    └── model_file_name.json

Available Script Commands

• npm run build: Compile the project.
• npm run start: Start the service.
• npm run dev: Development mode.

Security Precautions

• Protect your API key.
• Avoid exposing environment variables.
• Use secure communication protocols.
• Regularly update dependency libraries.

Troubleshooting

Common issues and solutions:

1. API connection failure:
   • Check the network status.
   • Confirm the validity of the API key.
2. Abnormal task execution:
   • Check the log files.
   • Verify the parameter configuration.
3. Data parsing error:
   • Validate the file format.
   • Ensure data integrity.

API Reference

Refer to the project documentation for detailed interface definitions.

Contribution Guidelines

We welcome code contributions:

1. Fork the repository.
2. Create a feature branch.
3. Commit your changes.
4. Submit a pull request.

📄 License

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