This article explains the differences between Function Calling, MCP, and A2A!

With the rapid development of AI technology, major technology giants have launched their own ecological standards to connect large language models with the outside world. The three most representative mechanisms are:

• Function Calling by OpenAI 
• MCP (Model Context Protocol) proposed by Anthropic 
• A2A (Agent-to-Agent Protocol) introduced by Google 

All three are trying to solve a core problem: to make the big model "dynamic" and connect to real-world data and tools . However, their starting points, solution paths, and application scenarios are different. This article will help you understand the core differences and connections between Function Calling, MCP, and A2A.

Function Calling: Adding plug-ins to large models

❝

OpenAI's Function Calling is designed to solve a classic problem: large model knowledge cannot be updated in real time .

✅ Main uses:

Let the large language model generate API call parameters through natural language  and indirectly access data from external systems, such as weather, stocks, databases, etc.

? Brief description of the workflow:

1. Function definition phase : The developer defines a function interface (such as get_current_weather), including parameter types and descriptions.
2. Reasoning phase : The model receives the user's question and determines whether a function needs to be called.
3. Parameter generation phase : The model outputs function parameters in JSON format.
4. Function execution phase : The system executes the actual function (calls the API) based on the parameters.
5. Result integration stage : The model generates natural language answers based on the returned results.

✅ Advantages:

• It's easy to get started, just define the JSON interface.
• When combined with a small number of functions, it has high development efficiency and is suitable for "small and beautiful" application scenarios.

❌ Limitations:

• Lack of standardization : Different model vendors have different interfaces, and developers need to adapt to multiple formats.
• Chain calls are not supported : developers are required to manually manage the calling process, and the model cannot automatically complete multi-step calling logic.

MCP: Make tool docking less painful

❝

MCP (Model Context Protocol) is a communication protocol launched by Anthropic, which aims to solve  the problem of lack of standards when connecting multiple models and multiple tools .

✅ Core concept:

Through standardized protocols, different large models (such as Claude, GPT, LLaMA) can be uniformly connected with different tools, greatly reducing integration costs.

? Architecture composition:

• MCP Host : such as IDE, Claude Desktop, user operation entrance.
• MCP Client : Maintains communication with MCP Server.
• MCP Server : A lightweight service that exposes functions to the outside world and connects models and data sources.
• Data source : can be a local file, database, or online service.

✅ Advantages:

• The standardized protocol solves the "one-to-many" docking problem in Function Calling.
• The cost of expanding new tools and new models is greatly reduced (from M×N to M+N).

❌ Limitations:

• MCP only solves the problem of “how to call tools” and does not support collaboration between agents.
• The learning cost is slightly higher for developers, and they need to understand the protocol structure.

A2A: Making Agent Collaboration Possible

A2A (Agent-to-Agent) is an open protocol proposed by Google. It mainly focuses on  how agents communicate and collaborate with each other and is the key foundation for multi-agent collaboration.

✅ Core concepts:

• Agent Card : The "electronic business card" of the Agent, describing its capabilities, interfaces, permissions, etc.
• A2A Server : Manages task distribution and collaborative execution.
• A2A Client : The application or agent responsible for initiating tasks.
• Task / Message : represents the task and the message exchange during the task process.

? Brief description of the workflow:

1. Initialization phase : The Client starts the task and sends an initial message to the Server.
2. Interaction phase : Interact with the Server during task execution.
3. Discovery phase : The Client checks the Agent Card to find the Agent with which it can collaborate.
4. Processing stage : Server schedules appropriate Agent to perform tasks.
5. Completion phase : The results are returned to the original Client.

✅ Advantages:

• It emphasizes autonomous collaboration between agents  and is suitable for multiple agents to collaborate to complete complex tasks.
• The task management process is standardized and the progress can be tracked.

❌ Limitations:

• The current ecosystem is not yet mature and support from mainstream manufacturers is limited.
• It has high requirements for scenarios and is suitable for large-scale, multi-module systems.

Comparison summary of the three

Relationship among the three: division of labor, collaboration, and future integration

The three can be understood as different levels of capabilities in the AI ​​system:

• Function Calling : "Point-to-point" calling between models and APIs.
• MCP : “Standardized access” for models and tools.
• A2A : “Collaboration and communication” of multiple intelligent agents.

In the future, we will likely see these three mechanisms merged into a unified system , such as a model calling a tool through MCP and collaborating with other models through A2A to complete a task. This will greatly enhance the automation capabilities of AI systems and their ability to handle complex tasks.

Summarize

Function Calling, MCP, and A2A respectively solve the three key problems of large model and tool calling, tool standard access, and agent collaboration. They each represent different levels of design ideas in AI engineering, and also indicate that future AI systems will evolve towards a smarter, more automated, and more collaborative direction.

❝

Technology is developing, protocols are evolving, and unification and integration will eventually be the general trend.