Recently, Google released the A2A protocol, extending the Model Context Protocol (MCP) proposed by Anthropic last year. Before diving into A2A, I wanted to take some time to document my notes on MCP.
While researching MCP, I came across many discussions on Medium and YouTube. In this article, beyond basic concepts and implementation details, I want to focus more on the ideas and design implications that are worth discussing.
📌 This article aims to move from fundamentals to deeper insights, showing that MCP is more than just “connecting multiple data sources or APIs for agents to automatically choose from.” Hopefully, it also won’t feel too dry or boring.
Friendly reminder: This article turned out to be longer than expected, so it’s split into two parts. Feel free to jump to the sections you’re most interested in 🥸
MCP stands for Model Context Protocol, an open protocol proposed by Anthropic in November 2024, designed to enable seamless integration between AI applications or agents and tools and data sources.
In everyday terms, you can think of MCP as the USB-C of AI applications. Just as USB-C provides a unified interface for connecting various devices to a computer, MCP standardizes how AI applications connect to external systems (as illustrated in the cover image: https://norahsakal.com/blog/mcp-vs-api-model-context-protocol-explained/).
From a software perspective, comparing MCP with APIs and LSP (Language Server Protocol) can make the idea easier to grasp👇
| API | LSP | MCP | |
|---|---|---|---|
| Concept | Standardizes how web applications interact with backends | Standardizes how IDEs interact with language server tools | Standardizes how AI applications interact with external systems |
| Key participants | Servers, databases, services | Code navigation, analysis, intelligence | Prompts, tools, resources |
If you’re wondering what kind of impact MCP standardization brings, keep reading.
From the comparison above, we can see that without MCP, building an AI application often requires extensive customization across many components. With MCP, however, there are significant gains across four different dimensions
| Role | Impact |
|---|---|
| AI application developers / engineers | Existing applications can be connected to MCP servers intuitively, with minimal extra work |
| MCP server or tool developers | Once built, an MCP server can be reused anywhere |
| End users | MCP enables richer and more powerful AI application scenarios |
| Enterprises | Clearer separation of responsibilities across AI product teams |