Your Conversational AI Glossary

Model Context Protocol (MCP)

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SYNTHEIA AI Glossary of Conversational AI Terms - AI Agent

What is MCP?

Model Context Protocol (MCP) is an emerging AI framework that enables large language models (LLMs) to share memory, coordinate reasoning, and interact within a unified conversational context. It defines how multiple AI agents or tools collaborate by referencing a shared model state, allowing for more coherent and personalized user experiences across applications.

How does Model Context Protocol work?

At its core, MCP introduces a standardized way for AI agents to access, modify, and reference shared memory or context. This memory could include user preferences, prior conversations, and task-specific instructions. MCP structures this information using “context containers” that models can read from and write to. These containers act like memory banks that persist across sessions or multiple agents, enabling continuity and collaboration between models—even when they are hosted on different platforms.

By decoupling memory and behavior from the model itself, MCP allows developers to orchestrate multi-agent workflows, route queries across specialized models, or switch between tools without losing context.

Why use Model Context Protocol?

  • Seamless Collaboration: Enables multiple models to work together by maintaining a shared state.

  • Personalization at Scale: Delivers highly tailored user experiences by remembering preferences across tools and sessions.

  • Modular Design: Facilitates switching between models or services without re-engineering core logic.

  • Future-Proofing: Lays the groundwork for multi-agent AI systems and complex, long-lived memory across interactions.

Real world examples

  • Multi-Agent Workflows: A legal assistant AI and a financial planner AI can work together using MCP to serve a client with integrated advice.

  • AI Assistants with Memory: Virtual agents can recall past interactions and apply them in new conversations without repeating data collection.

  • Cross-Tool Context Sharing: A model trained for document summarization can access previous notes generated by another model, ensuring continuity.

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