Advanced MCP Server

A comprehensive Model Context Protocol (MCP) server that provides advanced capabilities for web scraping, data analysis, system monitoring, file operations, API integrations, and report generation. Built with Python and designed for seamless integration with MCP-compatible clients like Claude Desktop.

🚀 Quick Start

The fastest way to get started is using the GitHub-hosted version:

{
  "mcpServers": {
    "advanced-server": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/hezronkimutai/mcp_server.git", "python", "advanced_mcp_server.py"],
      "disabled": false,
      "autoApprove": ["scrape_website", "analyze_data", "system_monitor", "file_operations", "api_integration", "database_query", "generate_report"]
    }
  }
}

Add this configuration to your MCP client (e.g., Claude Desktop) and start using the server immediately!

✨ Key Features

🌐 Web Scraping & Content Analysis

Advanced HTML parsing with BeautifulSoup4
Smart content extraction - links, images, and clean text
Automatic data persistence in SQLite database
Content analytics and comprehensive statistics
Duplicate detection and content deduplication

📊 Data Analysis & Visualization

CSV file analysis with pandas integration
Statistical insights - summaries, correlations, distributions
Trend detection and pattern analysis
Professional visualizations with matplotlib and seaborn
Export capabilities in multiple formats

🖥️ System Monitoring & Performance

Real-time monitoring of CPU, memory, and disk usage
Configurable intervals and monitoring duration
Historical data tracking with trend analysis
Performance alerts and threshold monitoring
Resource optimization recommendations

📁 Advanced File Operations

Intelligent file search with pattern matching
Content analysis and metadata extraction
Automated backup creation and management
Cleanup operations for temporary and cache files
File comparison and diff analysis
Batch operations support

🔌 API Integration & Caching

Full HTTP method support (GET, POST, PUT, DELETE, etc.)
Intelligent caching system with configurable TTL
Automatic retry logic with exponential backoff
Response validation and error handling
Rate limiting and request throttling

🗄️ Database Operations & Management

Built-in SQLite database for data persistence
Custom SQL query execution with safety checks
Data export utilities (CSV, JSON, XML)
Schema management and migrations
Query optimization and performance monitoring

📋 Comprehensive Report Generation

System health reports with actionable recommendations
Web scraping analytics and content summaries
Data analysis reports with statistical insights
Multiple output formats (Markdown, JSON, HTML, PDF)
Integrated charts and interactive visualizations
Automated scheduling and delivery options

📦 Installation

Prerequisites

Python 3.8+ (recommended: Python 3.11+)
pip package manager
Git (for GitHub installation)

Method 1: Direct GitHub Installation (Recommended)

No local setup required! The server runs directly from GitHub:

{
  "mcpServers": {
    "advanced-server": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/hezronkimutai/mcp_server.git", "python", "advanced_mcp_server.py"],
      "disabled": false,
      "autoApprove": ["scrape_website", "analyze_data", "system_monitor", "file_operations", "api_integration", "database_query", "generate_report"]
    }
  }
}

Method 2: Local Installation

For development or customization:

Clone the repository:

git clone https://github.com/hezronkimutai/mcp_server.git
cd mcp_server

Install dependencies:

pip install -r requirements.txt

Make executable (Unix/Linux/macOS):

chmod +x advanced_mcp_server.py

Add to MCP configuration:

{
  "mcpServers": {
    "advanced-server": {
      "command": "python",
      "args": ["C:/path/to/mcp_server/advanced_mcp_server.py"],
      "disabled": false,
      "autoApprove": ["scrape_website", "analyze_data", "system_monitor", "file_operations", "api_integration", "database_query", "generate_report"]
    }
  }
}

Method 3: Package Installation

Install as a Python package:

pip install git+https://github.com/hezronkimutai/mcp_server.git

Then use in your MCP configuration:

{
  "mcpServers": {
    "advanced-server": {
      "command": "advanced-mcp-server",
      "disabled": false,
      "autoApprove": ["scrape_website", "analyze_data", "system_monitor", "file_operations", "api_integration", "database_query", "generate_report"]
    }
  }
}

pip install -r requirements.txt

Add to your MCP configuration:

{
  "mcpServers": {
    "advanced-server": {
      "command": "python",
      "args": ["path/to/mcp_server/advanced_mcp_server.py"],
      "disabled": false,
      "autoApprove": [
        "scrape_website",
        "analyze_data", 
        "system_monitor",
        "file_operations",
        "api_integration",
        "database_query",
        "generate_report"
      ]
    }
  }
}

🛠️ Available Tools

🌐 scrape_website

Advanced web scraping with content analysis and database storage.

Parameters:

url (required): Target URL to scrape
extract_links (optional, default: false): Extract all hyperlinks
extract_images (optional, default: false): Extract image URLs and metadata
save_to_db (optional, default: true): Persist content to database

Real-world Examples:

// Basic website scraping
{
  "url": "https://example.com"
}

// Complete content extraction with database storage
{
  "url": "https://news.ycombinator.com",
  "extract_links": true,
  "extract_images": true,
  "save_to_db": true
}

// Blog post analysis
{
  "url": "https://blog.example.com/post/123",
  "extract_links": false,
  "extract_images": false,
  "save_to_db": true
}

📊 analyze_data

Comprehensive statistical analysis with professional visualizations.

Parameters:

file_path (required): Path to CSV file
analysis_type (required): Analysis type - "summary", "correlation", "distribution", "trends", "outliers"
create_visualization (optional, default: true): Generate charts and graphs
columns (optional): Specific columns to analyze
save_results (optional, default: true): Save analysis to database

Real-world Examples:

// Sales data analysis with trends
{
  "file_path": "/data/sales_2024.csv",
  "analysis_type": "trends",
  "create_visualization": true,
  "columns": ["revenue", "units_sold", "date"]
}

// Customer data correlation analysis
{
  "file_path": "/data/customers.csv",
  "analysis_type": "correlation",
  "create_visualization": true,
  "save_results": true
}

// Quick statistical summary
{
  "file_path": "/logs/performance_metrics.csv",
  "analysis_type": "summary",
  "create_visualization": false
}

🖥️ system_monitor

Real-time system performance monitoring with alerting.

Parameters:

duration_minutes (optional, default: 1): Monitoring duration
interval_seconds (optional, default: 10): Data collection interval
save_metrics (optional, default: true): Store metrics in database
alert_thresholds (optional): CPU/Memory/Disk usage alert levels
generate_report (optional, default: false): Create performance report

Real-world Examples:

// Quick system health check
{
  "duration_minutes": 2,
  "interval_seconds": 5,
  "save_metrics": true
}

// Extended monitoring with alerts
{
  "duration_minutes": 30,
  "interval_seconds": 60,
  "save_metrics": true,
  "alert_thresholds": {"cpu": 80, "memory": 90, "disk": 85},
  "generate_report": true
}

// Lightweight monitoring for scripts
{
  "duration_minutes": 1,
  "interval_seconds": 30,
  "save_metrics": false
}

📁 file_operations

Advanced file system operations with intelligent search and analysis.

Parameters:

operation (required): Operation type - "search", "analyze", "backup", "cleanup", "compare", "organize"
path (required): Target file or directory path
pattern (optional): Search pattern (supports regex and glob)
options (optional): Operation-specific configuration
recursive (optional, default: true): Include subdirectories
file_types (optional): Filter by file extensions

Real-world Examples:

// Find TODO comments in project
{
  "operation": "search",
  "path": "/project/src",
  "pattern": "TODO|FIXME|HACK",
  "options": {"case_sensitive": false, "context_lines": 2},
  "recursive": true,
  "file_types": [".py", ".js", ".ts"]
}

// Analyze project structure
{
  "operation": "analyze",
  "path": "/project",
  "options": {"include_stats": true, "generate_tree": true},
  "recursive": true
}

// Clean temporary files
{
  "operation": "cleanup",
  "path": "/project",
  "pattern": "*.tmp|*.cache|__pycache__",
  "options": {"dry_run": false, "backup_before_delete": true}
}

// Backup important directories
{
  "operation": "backup",
  "path": "/important/data",
  "options": {"destination": "/backups", "compression": "gzip", "exclude_patterns": ["*.log", "*.tmp"]}
}

🔌 api_integration

Robust API integration with intelligent caching and error handling.

Parameters:

endpoint (required): API endpoint URL
method (optional, default: "GET"): HTTP method
headers (optional): Request headers dictionary
data (optional): Request body (JSON, form data, or raw)
params (optional): URL query parameters
use_cache (optional, default: true): Enable response caching
cache_duration_hours (optional, default: 1): Cache TTL
timeout_seconds (optional, default: 30): Request timeout
retry_attempts (optional, default: 3): Number of retry attempts

Real-world Examples:

// Fetch weather data with caching
{
  "endpoint": "https://api.openweathermap.org/data/2.5/weather",
  "method": "GET",
  "params": {"q": "London", "appid": "your_api_key"},
  "use_cache": true,
  "cache_duration_hours": 1
}

// Post data to API with retry logic
{
  "endpoint": "https://api.example.com/users",
  "method": "POST",
  "headers": {"Content-Type": "application/json", "Authorization": "Bearer token"},
  "data": {"name": "John Doe", "email": "john@example.com"},
  "use_cache": false,
  "retry_attempts": 5,
  "timeout_seconds": 60
}

// GraphQL query
{
  "endpoint": "https://api.github.com/graphql",
  "method": "POST",
  "headers": {"Authorization": "Bearer github_token"},
  "data": {"query": "query { viewer { login } }"},
  "use_cache": true,
  "cache_duration_hours": 24
}

🗄️ database_query

Execute SQL queries with safety checks and result formatting.

Parameters:

query (required): SQL query to execute
table (optional): Target table for simple operations
limit (optional, default: 100): Maximum number of results
format (optional, default: "json"): Output format - "json", "csv", "html"
parameters (optional): Query parameters for prepared statements
export_file (optional): Save results to file

Real-world Examples:

// Analyze scraped content
{
  "query": "SELECT url, title, LENGTH(content) as content_length FROM web_scrapes WHERE scraped_at > datetime('now', '-7 days') ORDER BY scraped_at DESC",
  "limit": 50,
  "format": "json"
}

// System performance trends
{
  "query": "SELECT DATE(recorded_at) as date, AVG(cpu_percent) as avg_cpu, MAX(memory_percent) as max_memory FROM system_metrics WHERE recorded_at > datetime('now', '-30 days') GROUP BY DATE(recorded_at)",
  "format": "csv",
  "export_file": "/reports/performance_trends.csv"
}

// Search cached API responses
{
  "query": "SELECT endpoint, COUNT(*) as request_count, AVG(LENGTH(response_data)) as avg_response_size FROM api_cache GROUP BY endpoint",
  "limit": 20
}

📋 generate_report

Create comprehensive reports with visualizations and insights.

Parameters:

report_type (required): Report type - "system_health", "web_analysis", "data_summary", "api_usage", "custom"
format (optional, default: "markdown"): Output format - "markdown", "json", "html", "pdf"
include_charts (optional, default: true): Generate visualizations
time_range (optional): Data time range filter
save_to_file (optional): Save report to specified file
email_recipients (optional): Email addresses for report delivery
template (optional): Custom report template

Real-world Examples:

// Weekly system health report
{
  "report_type": "system_health",
  "format": "html",
  "include_charts": true,
  "time_range": "7_days",
  "save_to_file": "/reports/weekly_health_report.html"
}

// Web scraping analytics
{
  "report_type": "web_analysis",
  "format": "markdown",
  "include_charts": true,
  "time_range": "30_days",
  "save_to_file": "/reports/scraping_summary.md"
}

// Custom business intelligence report
{
  "report_type": "custom",
  "format": "pdf",
  "include_charts": true,
  "template": "business_dashboard",
  "save_to_file": "/reports/monthly_bi_report.pdf",
  "email_recipients": ["manager@company.com", "analyst@company.com"]
}

🗄️ Database Schema

The server automatically creates and manages an SQLite database with optimized tables:

📊 web_scrapes

Stores web scraping results with full-text search capabilities.

id: Primary key (INTEGER)
url: Scraped URL (TEXT, INDEXED)
title: Page title (TEXT)
content: Extracted text content (TEXT)
links_count: Number of extracted links (INTEGER)
images_count: Number of extracted images (INTEGER)
word_count: Content word count (INTEGER)
scraped_at: Timestamp (TIMESTAMP, INDEXED)

📈 system_metrics

Real-time and historical system performance data.

id: Primary key (INTEGER)
cpu_percent: CPU usage percentage (REAL)
memory_percent: Memory usage percentage (REAL)
disk_usage: Disk usage percentage (REAL)
network_io: Network I/O statistics (TEXT)
process_count: Active process count (INTEGER)
recorded_at: Timestamp (TIMESTAMP, INDEXED)

🔄 api_cache

Intelligent API response caching with TTL management.

id: Primary key (INTEGER)
endpoint: API endpoint URL (TEXT, INDEXED)
method: HTTP method (TEXT)
response_data: Cached response (TEXT)
response_status: HTTP status code (INTEGER)
response_size: Response size in bytes (INTEGER)
cached_at: Cache timestamp (TIMESTAMP, INDEXED)
expires_at: Cache expiration (TIMESTAMP, INDEXED)

📁 file_operations

File operation history and metadata.

id: Primary key (INTEGER)
operation_type: Operation performed (TEXT)
file_path: Target file/directory path (TEXT)
file_size: File size in bytes (INTEGER)
operation_result: Operation outcome (TEXT)
execution_time: Time taken in seconds (REAL)
created_at: Timestamp (TIMESTAMP, INDEXED)

🚨 Troubleshooting

Common Installation Issues

Python Version Compatibility

# Check Python version
python --version

# If using Python 3.8 or older, upgrade:
# Windows: Download from python.org
# macOS: brew install python@3.11
# Ubuntu: sudo apt install python3.11

Missing Dependencies

# Install all dependencies
pip install -r requirements.txt

# For specific missing packages:
pip install mcp aiohttp pandas matplotlib seaborn beautifulsoup4 psutil requests

# If still having issues, try:
pip install --upgrade pip
pip install --force-reinstall -r requirements.txt

Permission Errors (Unix/Linux/macOS)

# Make script executable
chmod +x advanced_mcp_server.py

# Or run with python directly
python advanced_mcp_server.py

MCP Configuration Issues

Server Not Starting

Check MCP client logs for error messages
Verify file paths in configuration are absolute
Test server manually:
python advanced_mcp_server.py # Should show server startup messages

Import Errors in GitHub Mode

// Add environment variables if needed
{
  "mcpServers": {
    "advanced-server": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/hezronkimutai/mcp_server.git", "python", "advanced_mcp_server.py"],
      "env": {
        "PYTHONPATH": ".",
        "PYTHONUNBUFFERED": "1"
      },
      "disabled": false
    }
  }
}

Tool Approval Issues

Make sure tools are in the autoApprove list:

"autoApprove": [
  "scrape_website",
  "analyze_data", 
  "system_monitor",
  "file_operations",
  "api_integration",
  "database_query",
  "generate_report"
]

Runtime Issues

Memory Usage

Large datasets: Process in chunks using limit parameters
Long monitoring: Use shorter intervals or duration
Database size: Regular cleanup of old data

Network Timeouts

Increase timeout values in API calls
Check firewall settings for outbound connections
Use smaller batch sizes for bulk operations

File Permission Errors

Windows: Run as administrator if needed
Unix/Linux: Check file/directory permissions
Use absolute paths to avoid relative path issues

Performance Optimization

Database Performance

# Regular maintenance (add to cron job)
python -c "
import sqlite3
conn = sqlite3.connect('server.db')
conn.execute('VACUUM')
conn.execute('ANALYZE')
conn.close()
"

Memory Management

Use streaming for large file operations
Clear visualization plots after generation
Implement result pagination for large queries

🔒 Security Considerations

Data Protection

Database encryption: Sensitive data is stored in local SQLite
API keys: Store in environment variables, not in code
File access: Server respects system file permissions
Input validation: All inputs are sanitized and validated

Network Security

HTTPS only: API calls use secure connections
Rate limiting: Built-in request throttling
Error handling: Sensitive information not exposed in errors
Caching: Sensitive API responses can be excluded from cache

Best Practices

Regular updates: Keep dependencies updated
Access control: Limit MCP server access appropriately
Monitoring: Use system monitoring for security events
Backups: Regular database and configuration backups

🤝 Contributing

We welcome contributions! Here's how you can help improve the Advanced MCP Server:

🚀 Quick Start for Contributors

Fork the repository on GitHub
Clone your fork locally:
git clone https://github.com/YOUR_USERNAME/mcp_server.git cd mcp_server
Create a feature branch:
git checkout -b feature/amazing-feature
Set up development environment:
pip install -r requirements.txt pip install -r requirements-dev.txt # If available

🛠️ Development Guidelines

Code Style

Follow PEP 8 Python style guidelines
Use type hints for function parameters and returns
Add docstrings for all functions and classes
Keep functions focused and single-purpose

Testing

# Run tests (when available)
python -m pytest tests/

# Manual testing
python advanced_mcp_server.py --test-mode

# Integration testing with MCP client

Adding New Tools

Define the tool in handle_list_tools():

Tool(
    name="your_new_tool",
    description="Clear description of what it does",
    inputSchema={
        "type": "object",
        "properties": {
            "parameter_name": {
                "type": "string",
                "description": "Parameter description"
            }
        },
        "required": ["parameter_name"]
    }
)

Implement the handler in handle_call_tool():

elif request.params.name == "your_new_tool":
    return await self.your_new_tool_method(request.params.arguments)

Create the method:

async def your_new_tool_method(self, args: Dict[str, Any]) -> List[types.TextContent]:
    """
    Implement your tool functionality here.
    
    Args:
        args: Dictionary of tool arguments
        
    Returns:
        List of TextContent with results
    """
    # Implementation here
    pass

Update documentation in README.md

Database Changes

Create migration scripts for schema changes
Maintain backward compatibility when possible
Document schema changes in commit messages

📝 Commit Guidelines

Use conventional commits:

git commit -m "feat: add new data visualization tool"
git commit -m "fix: resolve database connection timeout"
git commit -m "docs: update API documentation"
git commit -m "refactor: optimize file search algorithm"

🐛 Bug Reports

When reporting bugs, please include:

Operating System and Python version
MCP client being used (Claude Desktop, etc.)
Error messages and stack traces
Steps to reproduce the issue
Expected vs actual behavior

💡 Feature Requests

For new features, please:

Check existing issues to avoid duplicates
Describe the use case and problem you're solving
Provide examples of how the feature would be used
Consider backward compatibility

🔄 Pull Request Process

Update documentation as needed
Add/update tests for new functionality
Ensure all tests pass
Update CHANGELOG.md if applicable
Request review from maintainers

📋 Development Roadmap

Planned features and improvements:

Multi-format data export (Excel, Parquet, XML)
Advanced scheduling for automated reports
Plugin system for custom extensions
Web dashboard for server management
Docker containerization for easy deployment
Distributed caching with Redis support
Machine learning integration for data analysis
Real-time notifications and alerting
Authentication and authorization system
API rate limiting per client
Comprehensive test suite with CI/CD
Performance benchmarking tools

🏗️ Repository Structure

mcp_server/
├── 📄 advanced_mcp_server.py      # Main MCP server implementation
├── 📄 requirements.txt            # Python dependencies
├── 📄 setup.py                   # Package configuration
├── 📄 README.md                  # This documentation
├── 📄 CHANGELOG.md               # Version history (planned)
├── 📄 LICENSE                    # MIT License (planned)
│
├── 🗂️ configs/                   # Configuration files
│   ├── 📄 github_mcp_config.json      # GitHub deployment config
│   ├── 📄 local_mcp_config.json       # Local development config
│   ├── 📄 mcp_config_direct.json      # Direct execution config
│   └── 📄 working_mcp_config.json     # Working configuration
│
├── 🗂️ docs/                      # Documentation (planned)
│   ├── 📄 mcp_github_configs.md       # GitHub configuration guide
│   ├── 📄 api_reference.md            # API documentation (planned)
│   ├── 📄 examples.md                 # Usage examples (planned)
│   └── 📄 troubleshooting.md          # Troubleshooting guide (planned)
│
├── 🗂️ scripts/                   # Utility scripts
│   ├── 📄 github_mcp_runner.py        # GitHub execution wrapper
│   └── 📄 run_github_mcp.py          # Alternative runner
│
├── 🗂️ tests/                     # Test suite (planned)
│   ├── 📄 test_server.py              # Server tests
│   ├── 📄 test_tools.py               # Tool functionality tests
│   └── 📄 test_integration.py         # Integration tests
│
└── 🗂️ examples/                  # Usage examples (planned)
    ├── 📄 basic_usage.py              # Basic server usage
    ├── 📄 advanced_analysis.py        # Complex data analysis
    └── 📄 custom_tools.py             # Custom tool development

📁 Key Files Description

advanced_mcp_server.py: Core server implementation with all tools
requirements.txt: All Python dependencies with version constraints
setup.py: Package metadata and installation configuration
github_mcp_runner.py: Wrapper for running server from GitHub
Config files: Ready-to-use MCP configurations for different scenarios

🌟 Use Cases & Examples

📊 Business Intelligence Dashboard

# 1. Monitor system performance
{"duration_minutes": 60, "interval_seconds": 300, "save_metrics": true}

# 2. Analyze sales data
{"file_path": "/data/sales.csv", "analysis_type": "trends", "create_visualization": true}

# 3. Generate executive report
{"report_type": "data_summary", "format": "html", "include_charts": true}

🔍 Content Research & Analysis

# 1. Scrape competitor websites
{"url": "https://competitor.com", "extract_links": true, "save_to_db": true}

# 2. Analyze content patterns
{"query": "SELECT title, word_count FROM web_scrapes WHERE scraped_at > datetime('now', '-7 days')"}

# 3. Generate content report
{"report_type": "web_analysis", "format": "markdown", "time_range": "7_days"}

🔧 DevOps & System Administration

# 1. Monitor server health
{"duration_minutes": 1440, "interval_seconds": 600, "alert_thresholds": {"cpu": 80}}

# 2. Analyze log files
{"operation": "analyze", "path": "/var/log", "file_types": [".log"]}

# 3. Cleanup old files
{"operation": "cleanup", "path": "/tmp", "pattern": "*.tmp|*.cache"}

📈 Data Science Workflow

# 1. Import and analyze dataset
{"file_path": "/data/experiment.csv", "analysis_type": "summary"}

# 2. Generate correlation matrix
{"file_path": "/data/experiment.csv", "analysis_type": "correlation", "create_visualization": true}

# 3. Export results
{"query": "SELECT * FROM analysis_results", "format": "csv", "export_file": "/results/output.csv"}

🔗 MCP Ecosystem

Model Context Protocol - Official MCP documentation
MCP Python SDK - Python SDK for MCP
Claude Desktop - Popular MCP client

🛠️ Tools & Libraries Used

aiohttp - Async HTTP client/server
pandas - Data manipulation and analysis
matplotlib - Data visualization
BeautifulSoup - HTML parsing
psutil - System monitoring

🎓 Learning Resources

Python Async Programming - Asyncio documentation
SQLite Tutorial - SQLite learning guide
Data Analysis with Python - Pandas user guide

🆕 Changelog

Version 1.0.0 (Current)

✅ Initial release with core functionality
✅ Web scraping with content analysis
✅ Data analysis with visualizations
✅ System monitoring with metrics storage
✅ File operations with search capabilities
✅ API integration with caching
✅ Database operations with SQL support
✅ Report generation in multiple formats

Planned for Version 1.1.0

🔄 Enhanced error handling and recovery
🔄 Plugin architecture for extensibility
🔄 Web dashboard for server management
🔄 Docker support for containerized deployment
🔄 Advanced authentication and authorization

Planned for Version 1.2.0

🔄 Machine learning integration
🔄 Real-time notifications and alerts
🔄 Distributed caching with Redis
🔄 Performance optimization and benchmarking
🔄 Comprehensive test suite with CI/CD

⚖️ License

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

MIT License Summary

✅ Commercial use allowed
✅ Modification allowed
✅ Distribution allowed
✅ Private use allowed
❌ No warranty provided
❌ No liability assumed

🙏 Acknowledgments

💡 Inspiration & Thanks

Anthropic for creating Claude and the MCP standard
Model Context Protocol community for the excellent framework
Open source contributors of all the amazing libraries used in this project
Early adopters and testers who provided valuable feedback

🌟 Special Recognition

Python Software Foundation for the incredible Python ecosystem
SQLite team for the reliable embedded database
Matplotlib/Seaborn teams for powerful visualization tools
pandas team for making data analysis accessible

📞 Support & Community

🆘 Getting Help

📖 Documentation: Start with this README and the /docs folder
🐛 Issues: Report bugs and request features on GitHub Issues
💬 Discussions: Join the conversation on GitHub Discussions
📧 Email: For private inquiries, contact the maintainers

🌍 Community Guidelines

Be respectful and inclusive
Help others learn and succeed
Share knowledge and experiences
Contribute positively to the ecosystem
Follow the code of conduct

🔮 Future Vision

Our goal is to make the Advanced MCP Server the go-to solution for:

Data professionals needing comprehensive analysis tools
Developers requiring robust system integration
Businesses seeking automated reporting and monitoring
Researchers conducting data-driven investigations
DevOps teams managing complex infrastructures

⭐ If this project helps you, please give it a star! ⭐

🚀 Ready to supercharge your MCP experience? Get started now! 🚀

Made with ❤️ by the MCP Server community

This server cannot be installed

security - not tested

license - permissive license

quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

A comprehensive Model Context Protocol server providing capabilities for web scraping, data analysis, system monitoring, file operations, API integrations, and report generation.

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