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Agentic AI Era: Analyzing MCP vs MCO πŸš€ Hello everyone! With the rapid advancement of AI agent technology, two architectures have come into the spotlight: MCP (Model Context Protocol) and MCO (Model Context Open-json). Today, we’ll introduce the key features and differences of these two approaches. https://huggingface.co/spaces/VIDraft/Agentic-AI-CHAT MCP: The Traditional Approach πŸ›οΈ Centralized Function Registry: All functions are hardcoded into the core system. Static Function Definitions & Tight Coupling: New features require changes to the core application code, limiting scalability. Monolithic Design: Complex deployment and version management can cause a single error to affect the whole system. Code Example: '''py FUNCTION_REGISTRY = { "existing_function": existing_function, "new_function": new_function # Adding a new function } ''' MCO: A Revolutionary Approach πŸ†• JSON-based Function Definitions: Function details are stored in external JSON files, enabling dynamic module loading. Loose Coupling & Microservices: Each function can be developed, tested, and deployed as an independent module. Flexible Scalability: Add new features by simply updating the JSON and module files, without modifying the core system. JSON Example: [ { "name": "analyze_sentiment", "module_path": "nlp_tools", "func_name_in_module": "sentiment_analysis", "example_usage": "analyze_sentiment(text=\"I love this product!\")" } ] Why MCO? πŸ’‘ Enhanced Development Efficiency: Developers can focus on their own modules with independent testing and deployment. Simplified Error Management: Errors remain confined within their modules, enabling quick hotfixes. Future-Proofing: With potential features like remote function calls (RPC), access control, auto-documentation, and a function marketplace, MCO paves the way for rapid innovation. Practical Use & Community 🀝 The MCO implementation has been successfully tested on Vidraft’s LLM (based on Google Gemma-3)
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