MCP Series

A comprehensive collection of Model Context Protocol (MCP) servers that empower AI assistants with advanced capabilities to interact with external services, platforms, content sources, and real-time data.

About MCP Series

MCP Series is a professional organization dedicated to developing and maintaining enterprise-grade Model Context Protocol (MCP) servers. Our mission is to extend the capabilities of Large Language Models (LLMs) through rigorously designed integrations, enabling seamless connections between AI systems and a diverse range of external services, applications, and real-time data sources. We currently maintain a growing ecosystem of eight production-ready MCP servers with more integrations in active development.

What is the Model Context Protocol (MCP)?

MCP is an open protocol that enables AI models to securely interact with local and remote resources through standardized server implementations. This protocol focuses on production-ready and experimental MCP servers that extend AI capabilities through file access, database connections, API integrations, and other contextual services.

Our Repositories

Repository	Description	Key Features	Language	Owner
mcp-browser-agent	Browser automation and web interaction capabilities	Navigate to websites and control page interactions Fill forms and submit data Take screenshots and capture page content Execute JavaScript on web pages Perform API requests and process responses Support for multiple browser engines	TypeScript	@imprvhub
mcp-domain-availability	Domain availability checking and discovery	Check domain availability across popular TLDs Bulk domain verification support Intelligent domain suggestions Support for exact domain queries No external API dependencies	Python	@imprvhub
mcp-prompt-engineer	AI prompts and tools analysis across major platforms	Access prompts from Cursor, Windsurf, Replit, and major AI platforms Advanced search and comparison capabilities Professional analysis tools for prompt optimization Cross-platform prompt strategy evaluation Refine prompts based on highest standards from AI platforms	Python	@imprvhub & @nkapila6
mcp-status-observer	Real-time monitoring of major digital platforms	Track operational status across 20+ platforms Detailed component-level monitoring Status history and incident tracking Support for GitHub, Cloudflare, Vercel, LinkedIn, etc.	TypeScript	@imprvhub
mcp-local-rag	Local RAG-like web search without APIs	Primitive RAG-like web search functionality Runs completely locally without external APIs Docker and UV deployment support Semantic search capabilities Privacy-focused local processing	Python	@nkapila6
mcp-claude-spotify	Comprehensive Spotify control and management	Search tracks, albums, artists, and playlists Full playback control Create and manage playlists Personalized recommendations Access top played tracks	TypeScript	@imprvhub
mcp-meme-sticky	AI-powered meme generation and sticker creation	Create AI-generated memes using MCP Convert generated memes into stickers for Telegram WhatsApp sticker support (coming soon) No external APIs required Local meme generation capabilities	Python	@nkapila6
mcp-claude-hackernews	Browse and interact with Hacker News content	Access latest, top, and best-rated stories View story details and comments Clean formatting for improved readability Natural language query support	TypeScript	@imprvhub
mcp-rss-aggregator	Versatile RSS feed reader and content aggregator	Read articles from multiple RSS feeds OPML import support for existing subscriptions Category-based organization Source-specific filtering Well-formatted article presentation	TypeScript	@imprvhub

Technology Stack & Languages

Our MCP servers are built using modern technologies and programming languages to ensure optimal performance and developer experience:

TypeScript/Node.js Servers:

• Advanced TypeScript implementation with full type safety
• Node.js runtime for efficient server operations
• npm package management for easy distribution
• Modern ES modules and async/await patterns

Python Servers:

• Python 3.10+ with modern async capabilities
• UV package manager for fast dependency resolution
• Docker containerization for seamless deployment
• FastMCP and official Python MCP SDK integration

Contributing

We welcome contributions from developers passionate about expanding AI capabilities through MCP integrations. Our contribution process is designed to maintain high code quality while making it easy to get involved:

1. Fork the specific repository you're interested in
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Develop your contribution following our coding standards
4. Test thoroughly to ensure quality and compatibility
5. Commit your changes (git commit -m 'Add some amazing feature')
6. Push to your branch (git push origin feature/amazing-feature)
7. Submit a Pull Request with comprehensive documentation

We particularly value contributions that:

• Add new platform integrations
• Enhance existing functionality
• Improve error handling and resilience
• Optimize performance across different programming languages
• Extend documentation and examples
• Support cross-platform compatibility

Development Guidelines

For TypeScript/Node.js servers:

• Follow modern TypeScript best practices
• Use ESLint and Prettier for code formatting
• Implement comprehensive error handling
• Include type definitions for all interfaces

For Python servers:

• Follow PEP 8 style guidelines
• Use type hints throughout the codebase
• Implement proper async/await patterns
• Include comprehensive docstrings

Contact & Support

For questions, feedback, or collaboration inquiries:

• Open an issue in the relevant repository
• Check existing issues for potential solutions
• Provide detailed information when reporting problems

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The MCP Series organization is an independent initiative not affiliated with Anthropic or any AI assistant providers. We develop open tools that enhance AI capabilities through the Model Context Protocol standard.