Model Context Protocol

Model Context Protocol (MCP)

The Model Context Protocol (MCP) is an open standard designed to allow developers to establish secure, two-way connections between their data sources and AI-powered tools. Its architecture is simple: developers can expose their data via MCP servers or create AI applications (MCP clients) that connect to these servers.

This protocol allows you to build servers that expose data and functionality to LLM AI applications in a secure, standardized way. Think of it like a web API, but specifically designed for LLM interactions.

MCP Server and MCP Client

MCP Server

The MCP Server manages and maintains tools for the context information used by AI models. It stores, processes, and delivers contextual data to ensure that AI systems can generate relevant responses. The server handles multiple client requests, dynamically updates stored context, and ensures data consistency.

• Exposes Tools that connect with MCP hosts.
• Handles Resources and stores and manages contextual information.
• Processes requests from MCP Clients.
• Ensures data consistency and accuracy.
• Prompts: Pre-written templates that help users accomplish specific tasks

 

MCP Client

The MCP Client interacts with the MCP Server to retrieve and update contextual information. It sends requests to the server, receives processed data, and integrates the context into AI-driven applications. MCP Clients ensure that AI models can use stored context to provide meaningful and personalized responses.

• Requests context from the MCP Server.
• Updates context with new information.
• Integrates retrieved data into AI applications.
• Enhances response accuracy by utilizing stored context.

 

Advantages of Model Context Protocol (MCP)

Some of the advantages of MCP are as follows:

Enhanced Memory Management

• MCP allows AI models to retain and retrieve contextual information efficiently, enabling them to provide more consistent and relevant responses over time.

Improved User Experience

• By maintaining context, AI systems using MCP can offer more personalized interactions, making conversations feel natural and intuitive for users.

Better Decision-Making

• With structured context handling, AI models can analyze past interactions and use them to generate more informed and logical responses.

Optimized Performance

• MCP helps streamline AI operations by reducing redundant processing, improving efficiency, and minimizing unnecessary data retrieval.

Scalability

• AI systems using MCP can scale more effectively, as they can manage larger contexts without performance degradation, making them suitable for complex applications.

 

More information:

• https://www.anthropic.com/news/model-context-protocol