Model Context Protocol - Old wine in new bottle

 

Inspirations Behind the Model Context Protocol

Model Context Protocol appears to draw inspiration from several established protocols and architectural patterns in software engineering. Some of the key inspirations and the concepts MCP has adopted from them:

The brilliance of MCP is in how it combines these inspirations into a cohesive protocol specifically designed for the unique challenges of LLM context integration. Rather than reinventing the wheel, it takes established patterns that have proven successful in other domains and adapts them to the emerging requirements of AI applications.

What makes MCP unique is its focus on the specific needs of LLM applications, including:

• Clear security boundaries for sensitive data
• Standardised resource descriptions optimised for LLM consumption
• Bidirectional sampling capabilities that enable agentic patterns

Combination of established patterns with AI-specific requirements creates a protocol that feels familiar to developers while addressing the novel challenges of LLM integration.

What is the Model Context Protocol?

Model Context Protocol is a JSON-RPC based protocol designed to standardize communication between AI models and external systems. It enables AI models to access contextual information, tools, and resources from different providers through a unified interface. MCP essentially serves as a bridge, allowing models to extend their capabilities beyond their core training.

Key Parties in MCP

MCP involves two primary parties:

1. Client - Typically represents the AI model or the application hosting the model. The client initiates connections, makes requests for information, and may also receive requests from the server.
2. Server - Provides resources, tools, and contextual information that the client can access. Servers can be specialized providers of specific functionality or broader ecosystem components.

Core Concepts in MCP

Key Parties in MCP

MCP Workflow

How does tools work

Tool is key abstraction in MCP that connect LLM to real world and give it capability to have information from outside world and also to take action.

Tool matching 

You might have question how what happens before tool invocation. How does LLM select which tool to use, now this could be very specific to underlying LLM but it will use some algorithm based on few core ideas.

Conclusion

Model Context Protocol (MCP) has captured widespread attention, highlighted by Google's recent Agent-2-Agent protocol release. The buzz around this is palpable, with LLM tools and companies making significant investments, anticipating it as the next major leap in Generative AI with the potential to unlock numerous use cases for working with Large Language Models.

While MCP undoubtedly solves an important integration challenge for LLMs, the fundamental question remains: what capabilities will these MCP servers or other implementations actually expose in terms of manipulating and enriching the interaction with LLMs? If these capabilities address only trivial or low-impact problems, our focus should arguably be on leveraging these transformative technologies to build truly innovative ("zero to one") capabilities that fundamentally change how we work with LLMs, rather than simply creating new interfaces for existing ones. 

So, the answer to my question about "old wine in a new bottle" is yes. The bottle is indeed shiny, creating a strong desire for it, much like the latest tech gadget.