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2977
Agentic AI Era: Analyzing MCP vs MCO 🚀
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.
VIDraft/Agentic-AI-CHAT
MCP: The Traditional Approach 🏛️
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 🆕
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? 💡
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)
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.
VIDraft/Agentic-AI-CHAT
MCP: The Traditional Approach 🏛️
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 🆕
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? 💡
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)
liked
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openfree's
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Post
2977
Agentic AI Era: Analyzing MCP vs MCO 🚀
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.
VIDraft/Agentic-AI-CHAT
MCP: The Traditional Approach 🏛️
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 🆕
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? 💡
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)
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.
VIDraft/Agentic-AI-CHAT
MCP: The Traditional Approach 🏛️
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 🆕
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? 💡
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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