What is falahgs Gemini Data Analysis Research MCP Server
Gemini Data Analysis & Research MCP Server
A powerful Model Context Protocol (MCP) server that leverages Google's Gemini Flash 2 AI model for comprehensive data analysis, research paper generation, and automated email delivery. This server provides an integrated solution for analyzing datasets, generating research content, and distributing results directly to stakeholders via email.
๐ Features
1. Advanced Data Analysis & Reporting (analyze-data
)
- Comprehensive analysis of Excel (.xlsx, .xls) and CSV files
- Features:
- Automatic data type detection and parsing
- Statistical analysis of numeric columns
- Interactive visualizations using Chart.js
- AI-powered insights using Gemini Flash 2
- Detailed HTML reports with interactive plots
- Direct email delivery of analysis results
- Basic and detailed analysis modes
- Customizable output directory
- Support for large datasets
- Automatic outlier detection
- Correlation analysis for numeric columns
2. Research & Email Delivery System (send-email
)
- Professional research paper generation and distribution
- Features:
- AI-powered research paper generation
- Automated email delivery of analysis results
- Support for multiple content types:
- Research papers
- Technical reports
- Data analysis summaries
- Business intelligence reports
- Professional email subject line generation
- Support for both HTML and plain text content
- Image attachments with inline display capability
- Secure SMTP authentication
- Comprehensive error handling and status reporting
- Professional email formatting
- Message delivery tracking
- Customizable email templates
3. Research & Analysis Generator (generate-thinking
)
- Advanced research and analysis generation
- Features:
- Research paper generation
- Technical documentation writing
- Data analysis summaries
- Business intelligence reports
- Timestamped response saving
- Customizable output directory
- Direct email delivery of generated content
- Professional content creation
๐ Quick Start
Prerequisites
- Node.js (v16 or higher)
- TypeScript
- Claude Desktop
- Google Gemini API Key
- SMTP Email Account (for email functionality)
Installation
- Clone and setup:
git clone [your-repo-url]
cd gemini-data-analysis-email-generator
npm install
- Create
.env
file:
GEMINI_API_KEY=your_api_key_here
[email protected]
NODEMAILER_PASSWORD=your_app_password_here
- Build the project:
npm run build
Claude Desktop Configuration
- Create/Edit
%AppData%/Claude/claude_desktop_config.json
:
{
"mcpServers": {
"Gemini Data Analysis": {
"command": "node",
"args": ["path/to/gemini-data-analysis-email-generator/dist/index.js"],
"cwd": "path/to/gemini-data-analysis-email-generator",
"env": {
"GEMINI_API_KEY": "your_api_key_here",
"NODEMAILER_EMAIL": "[email protected]",
"NODEMAILER_PASSWORD": "your_app_password_here"
}
}
}
}
- Restart Claude Desktop
๐ Using the Tools
Data Analysis with EDA and AI
{
"name": "analyze-data",
"arguments": {
"fileData": "base64_encoded_file_content",
"fileName": "data.xlsx",
"analysisType": "detailed",
"outputDir": "./analysis_results"
}
}
Email Sending with AI Subject Generation
{
"name": "send-email",
"arguments": {
"to": "[email protected]",
"subjectPrompt": "Create a professional subject line for a business report",
"text": "Hello! This is the plain text version of our email.",
"html": "<h1>Hello!</h1><p>This is the <b>HTML</b> version of our email.</p>",
"images": [
{
"name": "chart.png",
"data": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA..."
}
]
}
}
Thinking Generation
{
"name": "generate-thinking",
"arguments": {
"prompt": "Analyze the market trends for Q1 2024",
"outputDir": "./thinking_output"
}
}
๐ Output Structure
output/
โโโ analysis/
โ โโโ plots/
โ โ โโโ column1_histogram_[timestamp].html
โ โ โโโ column2_histogram_[timestamp].html
โ โโโ analysis_[timestamp].txt
โ โโโ report_[timestamp].html
โโโ thinking/
โ โโโ gemini_thinking_[timestamp].txt
โโโ emails/
โโโ email_log_[timestamp].txt
๐ ๏ธ Development
Available Scripts
npm run build
: Compile TypeScript to JavaScriptnpm run start
: Start the MCP servernpm run dev
: Run in development mode with ts-node
Environment Variables
GEMINI_API_KEY
: Your Google Gemini API keyNODEMAILER_EMAIL
: Your email address for sending emailsNODEMAILER_PASSWORD
: Your email app password (for Gmail, use an app password)
๐ Security Notes
- Store your API keys securely
- Don't share your
.env
file - For Gmail, use app passwords instead of your main account password
- Be careful with the content of emails sent through the system
- Never include sensitive or personal information in email examples
๐ Troubleshooting
Common Issues
-
API Key Error
- Verify
.env
file exists - Check API key validity
- Ensure proper environment loading
- Verify
-
Claude Desktop Connection
- Verify config.json syntax
- Check file paths in config
- Restart Claude Desktop
-
Email Sending Issues
- Check that NODEMAILER_EMAIL and NODEMAILER_PASSWORD are set correctly
- For Gmail, ensure you've created an app password
- Verify that less secure app access is enabled for non-Gmail providers
- Check recipient email address format
-
Data Analysis Issues
- Ensure file format is supported (.xlsx, .xls, .csv)
- Check file encoding (UTF-8 recommended)
- Verify file size is within limits
- Ensure numeric columns are properly formatted
Debug Mode
Add DEBUG=true
to your .env
file for verbose logging:
GEMINI_API_KEY=your_key_here
DEBUG=true
๐ API Reference
Data Analysis Tool
interface AnalyzeDataParams {
fileData: string; // Base64 encoded file content
fileName: string; // File name (must be .xlsx, .xls, or .csv)
analysisType: 'basic' | 'detailed'; // Analysis type
outputDir?: string; // Optional output directory
}
Email Sending Tool
interface SendEmailParams {
to: string; // Recipient email address
subjectPrompt: string; // Prompt for Gemini to generate email subject
text: string; // Plain text version of email
html?: string; // HTML version of email (optional)
images?: { // Optional images to attach
name: string; // Image filename
data: string; // Base64 encoded image data
}[];
}
Thinking Generation Tool
interface GenerateThinkingParams {
prompt: string; // Analysis prompt
outputDir?: string; // Optional output directory
}
๐จโ๐ป Author
Falah G. Salieh
๐ Baghdad, Iraq
๐
2025
๐ค Contributing
- Fork the repository
- Create your feature branch
- Commit your changes
- Push to the branch
- Create a Pull Request
๐ License
MIT License - See LICENSE file for details
Leave a Comment
Frequently Asked Questions
What is MCP?
MCP (Model Context Protocol) is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications, providing a standardized way to connect AI models to different data sources and tools.
What are MCP Servers?
MCP Servers are lightweight programs that expose specific capabilities through the standardized Model Context Protocol. They act as bridges between LLMs like Claude and various data sources or services, allowing secure access to files, databases, APIs, and other resources.
How do MCP Servers work?
MCP Servers follow a client-server architecture where a host application (like Claude Desktop) connects to multiple servers. Each server provides specific functionality through standardized endpoints and protocols, enabling Claude to access data and perform actions through the standardized protocol.
Are MCP Servers secure?
Yes, MCP Servers are designed with security in mind. They run locally with explicit configuration and permissions, require user approval for actions, and include built-in security features to prevent unauthorized access and ensure data privacy.
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