What is Model Context Protocol (MCP)?

MCP, or Model Context Protocol, is a framework that gives structure to how AI understands and remembers ongoing conversations and user preferences. Think of it as a memory system and rulebook that helps AI stay consistent and relevant. In fact, this layer can be described as part of the token generation process. Generally, we have a set of commands that perform specific tasks—such as retrieving resources, uploading content, downloading files, and so on. To accomplish these, we rely on various APIs. Now, if we are dealing with different types of AI models or agents, we need a form of standardization. In other words, a protocol or set of instructions is required. This is where the middle layer comes in. It acts as an intermediary that interprets user commands (or LLM outputs) and invokes the appropriate API, tool, or function for each request. Key components include: 1) Token Generation and Chain-of-Thought Reasoning: Generated based on the context. 2) Tool Usage Recognition: Identifying when to trigger external tools like web search, image creation, or code execution. 3) Tool Invocation Process: Generating tool call commands, executing them via a controller, and integrating the results. 4) Modularity and Flexibility: Ensuring the system can adapt to diverse inputs and tasks through interchangeable components.