Introduction: The Dawn of Collaborative AI

Think about how you collaborate with colleagues to complete complex projects — you share information, ask questions, and combine your expertise. Now imagine these AI agents can do the same thing, but instead of working in isolation, they can work together to solve problems.

^{This is exactly what the Agent-to-Agent (A2A) protocol [1],} released by Google on April 9, 2025, is pursuing. The protocol transforms AI agents working alone into team collaborators: research assistants can seamlessly pass research results to copywriting assistants, and travel planning assistants can directly check hotel budgets with financial assistants - without humans playing the role of middlemen. The enthusiasm of the developer community has already proven its value. The fact that the protocol has received 7,000+ Stars on GitHub within a few days of its launch ^{[2] is enough to speak for itself. However, it should be noted that the Model Context Protocol (MCP) [3]}  recently launched by Anthropic also has a similar vision.

How should developers choose? Is A2A just MCP with a different name? Should developers learn one protocol over the other? Or do the two protocols serve different scenarios?

This tutorial will cut through the hype and help you:

This article won’t contain too much technical jargon, only clear insights into how these protocols are reshaping the AI ​​collaboration paradigm. After reading this article, I believe you will have a precise grasp of the unique advantages of A2A over MCP (and vice versa). Are you ready to explore this technological craze? Let’s take a look!

MCP vs. A2A

Let's say you're planning a dream trip to Hawaii. There's so much to consider! You'll need:

These tasks involve completely different areas of expertise! Now imagine you have an AI agent named Claude. You can ask: "Claude, what will the weather be like in Maui next week?"

But the problem is: Claude is trained based on past data, and has no idea about the real-time weather conditions, nor can it predict the weather for next week. This is like asking your smart friend who hasn’t visited the city recently - they really have no way of knowing! This is when the two technical protocols discussed in this article are needed to break the deadlock.

2.1 MCP: Giving AI Agents Superpowers

The Model Context Protocol (MCP) is like equipping any AI Agent with the superpower of "calling professional tools on demand".

A conversation scenario without MCP enabled might look like this:

You asked: "What will the weather be like next week on Maui?"

Claude answered: "I can't get real-time weather data or forecasts, so I suggest you check the weather service website."

This answer is obviously disappointing. But after enabling MCP:

You asked: "What will the weather be like on Maui next week?"

Claude Answer: [Connect to the weather service via MCP] "The latest weather forecast for Maui next week calls for sunny skies with temperatures around 82°F, and a brief shower possible Wednesday afternoon."

There are four simple steps behind this process:

1)  Discover available “superpowers” . Claude searches the tool library and asks, “What cool tools can I use today?” and discovers a weather service that can be used. (Code implementation: call list_tools ^[4] )

2)  Construct a service request . Claude generates a precise instruction: "Hey, Weather Tool, please provide the weather forecast for Maui next week" (but this instruction is expressed in computer language) (code implementation: call_tool ^[5] )

3)  Get an expert-level response . The weather service works its magic and returns the response: "Maui: 82°F, sunny, with a chance of light rain Wednesday afternoon!" (Code implementation: execute Tool.run ^[6] )

4)  Translate into human language . Claude converts professional weather forecasts into easy-to-understand conversational chat responses (code implementation:  get the results from call_tool ^{[7] )}

The magic here is that MCP builds a standardized channel for any AI agent to interact with any tool. It's like building a universal adapter that fits all appliances and sockets.

2.2 A2A: Building Your AI Dream Team

But when it comes to complex Hawaii itinerary planning, it is not enough to just get weather data - you also need to integrate flight and hotel reservations, arrange activities, control budgets and other professional capabilities. This is where the Agent-to-Agent (A2A) protocol comes into play. If MCP is a bridge connecting AI with various tools, A2A is used to build a collaborative network between professional AI Agents.

Imagine if you had a travel dream team like this:

With A2A, your Personal Agent can delegate vacation planning tasks to these experts and integrate their professional advice.

A conversation without A2A might look like this:

You ask: "Please plan a 5-day trip to Hawaii for me in June with a budget of $3,000."

Personal assistant answer: "I can provide some general recommendations, but I don't have access to real-time flight prices or hotel availability. For activities, I can recommend some popular options, but I can't be sure if they will fit your travel dates or budget."

This is obviously not very helpful for having a dream vacation! But with A2A enabled:

You ask: "Please plan a 5-day trip to Hawaii for me in June with a budget of $3,000."

Personal assistant answer: [Scheduling Expert Assistant via A2A] "I have prepared a complete 5-day itinerary for you in Hawaii in June! Round-trip airfare from LAX ($650) and a beachfront hotel in Maui ($1200) total $1850, leaving $1150 for activities. The activities are as follows: snorkeling at Molokini Crater on the second day ($145/person), traditional Luau dinner on the third day ($120/person), and a road trip to Hana on the fourth day ($210/person). The remaining approximately $675 can be used for meals and souvenirs. Do you want me to book any of these for you?"

Behind the scenes, your Personal Agent is orchestrating this holiday symphony via the A2A protocol:

1)  Find the right expert. The personal assistant flips through its digital "business card book" (AgentCard ^[8] ) and selects expert assistants who can help with travel, event planning , and budgeting (code implementation: call A2ACardResolver ^[9]  to get the card)

2)  Task decomposition and assignment. The personal assistant breaks down your holiday dream into multiple easy-to-manage tasks:

4)  Repeat the above steps in the "Activity Arrangement" and "Budget Preparation" stages

Ultimately, your personal assistant combines all that expert knowledge into one coherent travel plan. You don’t have to get involved – you just sit back and enjoy the complete itinerary carefully crafted by our team of experts!

2.3 Perfect combination

When MCP and A2A work together, real magic happens:

It’s like having a team of expert consultants (A2A) and equipping each consultant with a dedicated work device (MCP). Together, they can complete complex tasks that a single AI Agent cannot handle on its own.

The key difference is who is talking to whom:

The best part is its scalability. Through unified standards, "one-time development, universal adaptation" is achieved - any newly developed tool or intelligent agent can be seamlessly integrated into the existing ecosystem, and different components are like Lego blocks with standardized interfaces, without the need to repeatedly develop docking programs for specific combinations.

Beyond Tools vs. Agents:

Introducing their real technical differences

Although the explanation that "in MCP, agents talk to tools, and in A2A, agents talk to other agents" is easy to understand, you may wonder: "Is this division too simple?"

That’s right! The line between “tool” and “agent” is actually very blurred. In fact, as OpenAI’s documentation shows ^[17] , we can even use entire agents as tools! Imagine having your AI agent use another AI agent as a tool — this operation immediately triggers an exponential stacking of system levels.

If you want to go beyond the superficial explanation and get into the technical details, you have come to the right place. Let’s move beyond the superficial differences of “who talks to whom” and analyze the code-level features that make these two protocols fundamentally different!

Important note: The following analysis is based on Zach's personal understanding during the actual development process, and some details may be wrong! If you find any problems, please leave a message to discuss - this is the best way for us to make progress together.

3.1 Difference 1: Natural Language and Structured Patterns

With A2A, you simply say "How many Canadian dollars are in 100 USD?" With MCP, you must enter the exact format required by the currency tool - for example {"currency_from": "USD", "currency_to": "CAD", "amount": 100}. It's a completely different way of communicating!

A2A communication is similar to human conversation

A2A uses a method similar to natural human conversation:

# A2A Client sending a task
user_message  =  Message (
    role = "user" ,
    parts = [ TextPart ( text = "How much is 100 USD in CAD?" )]
)

The agent receiving the message can understand the request in the most natural way - just like asking a friend for help, there is no strict format requirement.

MCP requires precise parameters

MCP requires strict matching of parameters in a predefined schema:

# MCP Client calling a tool
tool_name  = "get_exchange_rate"
# Must match EXACTLY what the tool expects
arguments  = { "currency_from" : "USD" , "currency_to" : "CAD" }

If you don't provide the exact parameters the tool expects (for example, using "from_currency" instead of "currency_from"), the call will fail. No wiggle room here!

Why This Matters

Imagine a customer service scenario: A2A is like a customer service representative who can communicate with you naturally and accurately understand your needs even if your question cannot be categorized into the preset tags; MCP is like filling out a form that must be strictly checked and only specified content can be entered in specific fields.

A2A's natural language approach can handle "non-standard" requests and adapt to new scenarios - for example, when asked "Is $100 enough for dinner for two in Toronto?", the natural language agent can combine currency knowledge and local restaurant pricing. MCP's strict model ensures reliable and predictable results - ideal for scenarios that require precise answers to standard questions.

3.2 Difference 2: Task Lifecycle and Function Call

A2A treats work as complete tasks with a lifecycle, while MCP treats them as independent function calls. This is similar to the difference between a project manager (A2A) and a calculator (MCP).

A2A multi-stage task management

The core of A2A is built around the concept of a "task", which has a complete lifecycle with multiple states:

# A2A Task has explicit states in its lifecycle
{
"id" : "task123" ,
"status" : {
"state" : "running" , # Can be: pending → running → completed/failed
"startTime" : "2025-04-12T10:30:00Z"
},
"artifacts" : [...] # Partial results as they're created
}

Tasks can naturally flow through different states — starting, requiring more input, producing partial results, and finally completing or failing. The A2A protocol manages this lifecycle natively, making it well suited for complex, multi-stage work with uncertainty.

Single-stage operation of MCP

MCP operations are essentially single-phase - they either succeed or fail:

# MCP has no built-in "in-progress" or "needs more input" states
result  =  client . call_tool ( "get_exchange_rate" , { "from" : "USD" , "to" : "CAD" })

# If arguments are invalid or missing, it simply fails with an error
# There's no protocol-level "input-required" state

If the tool needs more information, it cannot request it directly through the protocol - it must return an error message, or create a new, separate request that is handled by the client application. Progress updates are an optional extra, not a core part of the operation lifecycle.

Why This Matters

Imagine the difference between renovating your house and using individual tools. A2A is like hiring one contractor to manage the entire renovation project — they handle all phases, keep you updated on progress, and troubleshoot any issues that arise. MCP is like calling each specialist individually — the plumber does one thing, the electrician does another, and no one is keeping track of the overall project progress.

A2A excels in work that requires adaptability, involves uncertainty, and involves complex, multi-stage tasks, such as project research, creative work, or complex problem solving.

MCP excels when precise, independent operations with predictable inputs and outputs are required, such as data computation, data retrieval, or certain types of data transformation.

3.3 Difference 3: Macro-level, general skills vs. clear, step-by-step, specific functions

A2A describes in general terms what the agent can do, while MCP details the specific functions available and how to call them.

A2A Capabilities Description

A2A describes an agent's capabilities in general, flexible terms:

# A2A AgentCard example
agent_skill  =  AgentSkill (
id = "research_capability" ,
    name = "Research and Analysis" ,
    description = "Can research topics and provide analysis." ,
    examples = [
"Research quantum computing advances" ,
"Analyze market trends for electric vehicles"
]
# Notice: no schema for how to invoke these skills!
)

It's like a resume, telling you what someone is good at, rather than how to assign work to them.

Functional Specifications of MCP

MCP defines exactly what each available function is:

# MCP tool definition
{
"name" : "get_exchange_rate" ,
"description" : "Get exchange rate between currencies" ,
"inputSchema" : {
"type" : "object" ,
"properties" : {
"currency_from" : { "type" : "string" },
"currency_to" : { "type" : "string" }
},
"required" : [ "currency_from" , "currency_to" ]
}
}

It's like an instruction manual, leaving no room for interpretation.

Why This Matters

Imagine a work assignment scenario. Using A2A is like just telling a team member "you are responsible for the research part of this project" - they will plan the implementation details themselves based on their expertise. Using MCP is like giving detailed step-by-step instructions: "search these three databases with these three keywords and organize the results in this format."

The A2A model encourages creative thinking, initiative, and the ability to respond to unexpected situations. The agent can dynamically adjust its strategy based on new information discovered during exploration.

The MCP model ensures consistency and predictability of the execution process, as well as precise control over the operation process and results. This approach is particularly suitable for scenarios where established procedures need to be strictly followed.

The difference between the two concepts

These three technical differences reflect different philosophies on AI collaboration:

MCPs can be likened to Lego blocks – precise, predictable, and can be put together in a clear way. A2A is more like clay – flexible, adaptable, and capable of taking on shapes you might not have initially imagined.

The beauty of this is that you don’t have to choose one or the other! Many complex systems will use both modes: MCP for precise tool execution, and A2A for collaborative problem solving between specialized agents.

Conclusion: Collaborative AI is the future

We have dissected the core differences between A2A and MCP, and the essence of this is that they are not either-or technical standards, but complementary solutions that solve different parts of the same problem.

Think of MCP as a builder of precision tools and A2A as a builder of expert teams. When the two work together, the real magic happens:

For developers, this means that they don't have to worry about artificially created protocol camps. Instead, you can ask yourself: "Do I need precise tool execution (MCP), agent collaboration (A2A), or both?" The answer depends entirely on your specific application scenario.

How will the future develop? As the AI ​​ecosystem matures, the integration of these two protocols will deepen. The companies behind these protocols understand that the ability to work together is the future trend - isolated AI agents will eventually be eliminated. The most valuable applications will not be limited to a single agent with a few tools, but will coordinate a complete team of professional agents. If developers can grasp this intersection of technology, they will become pioneers in building the next generation of truly collaborative AI systems.