LLM is a point, MCP is a line, A2A is a surface!

From single-point intelligence to multi-dimensional collaboration, we are witnessing the continuous expansion of the spatial dimension of artificial intelligence.

LLM is a point, MCP is a line, and A2A is a surface .

These three key technologies together build the dimensions of the modern AI collaborative ecosystem.

On April 10, 2025, Google released the open protocol Agent2Agent (A2A), which aims to solve the interoperability problem between different intelligent agents. It received support from many companies at the beginning of its release.

The reason for such a quick response is that the A2A protocol allows AI agents to communicate with each other, exchange information securely, and coordinate actions on various enterprise platforms or applications.

It connects isolated "lines" into a collaborative "surface", enabling multiple agents with different capabilities to work together.

A2A is a higher level of abstraction of MCP, while MCP is an LLM-level operation tool and data, and A2A enables applications and intelligent agents to communicate with each other.

Let’s analyze these three concepts, explore how they complement each other, and show how they work with code examples.

LLM is a point: an isolated unit of intelligence

The essence of LLM

Large Language Models (LLMs) such as ChatGPT, Claude, Gemini, etc. are essentially isolated intelligent computing units. They have powerful language understanding and generation capabilities, but by default they are like isolated "brains" that cannot actively obtain external information or perform external operations .

There are more than 200 large models on the market, including commercial ChatGPT, GPT-4o, Claude 3.5, Google Gemini, and open source Llama 3.3, Qwen 2.5, etc. Although these models have different capabilities, they all face the same limitation: they are trapped in their own "knowledge islands".

Limitations of LLM

Although LLM is powerful, it faces several core problems:

• Knowledge Deadline : No Access to New Information Post-Training
• No access to proprietary data : No direct query of internal corporate databases
• Unable to perform operations : Cannot directly call API or control external systems
• Unable to continuously update : Lack of continuous learning mechanism

If LLM is compared to a point, it is isolated, static, closed, unable to establish connections with the outside world, can only answer questions based on existing knowledge, cannot access external resources or perform operations, just like an isolated point, valuable but unconnected .

MCP is the bridge between models and tools

Definition and Value of MCP

Model Context Protocol is an open protocol launched by Anthropic in 2024 to solve the problem of connecting LLM with the outside world.

MCP is clearly positioned as a "USB-C interface for AI applications", providing a standardized way to connect AI models to different data sources and tools. It establishes a "line" from the model to external resources, enabling LLM to break through its own limitations.

MCP attempts to transform the "M×N problem" of integrating AI applications with external tools and systems into an "M+N problem". Tool creators build N MCP servers and application developers build M MCP clients, thereby solving the complexity of integration.

Core components of MCP