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MCP vs A2A Protocol
How MCP and A2A Protocols Work Together (or Replace Each Other) in the AI Agent Ecosystem
Gargi Gupta & Shubham Saboo
April 16, 2025
If you've spent any time in the AI developer community lately, you've probably noticed the growing buzz (and confusion) around two protocols with very similar-sounding roles: MCP and A2A.
First, Anthropic released the Model Context Protocol (MCP) last November, which quickly gained traction with developers building AI agents that need to connect to external data and tools. Then just last week, Google released the Agent2Agent (A2A) Protocol, with over 50 tech partners backing it from day one.
The announcement of A2A created immediate questions and debates. Is this complementing MCP as Google claims, or are we seeing the beginning of a protocol war? Is there genuine differentiation in use cases, or is this about tech giants staking their claims in the evolving AI ecosystem?
Google says A2A ❤️ MCP, with each solving different problems in the agent ecosystem. But it’s not as black and white as Google positioned it.
This isn't the first time we've seen overlapping standards in tech (remember SOAP vs. REST?), and it likely won't be the last. What's important is understanding what each protocol actually does, where they potentially overlap, and how they might fit into your AI development stack.
In this post, we'll break down what each protocol does, how they relate to each other in a typical AI agent pipeline, and why understanding both is crucial if you're building the next generation of AI applications. We'll present multiple perspectives so you can make informed decisions about which approach makes sense for your projects.
Understand MCP from the Foundation
Before MCP, every integration was a custom job. Want your LLM to query a database? Write a connector. Need it to access your Google Drive? Build another connector. Connecting to your GitHub repo? You guessed it - more custom code. This led to a fragmented landscape where each AI application had its own proprietary ways of connecting to external resources.
As Anthropic puts it, MCP is like "a USB-C port for AI tools." Just as USB-C provides a standardized way to connect devices to various peripherals, MCP standardizes how AI models connect to different data sources and tools.
At its core, MCP follows a client-server architecture:
MCP Hosts: Programs like Claude Desktop or IDEs that want to access data through MCP
MCP Clients: Protocol clients that maintain connections with servers
MCP Servers: Lightweight programs that expose capabilities through the standardized protocol
MCP servers can expose three main elements:
Resources: Data sources like files, databases, or API responses
Tools: Executable functions that LLMs can call
Prompts: Reusable templates for common LLM interactions
Think of it as a universal way for the robots to read instruction manuals (tools) and understand the information they need to work with (data). For example, if an agent needs to search for information on the web, MCP helps the underlying AI model know how to use the "search" tool and understand the search results.
The critical distinction here is in how control is distributed. Resources are application-controlled (the app decides which resources to use), tools are model-controlled (the AI decides which tools to use), and prompts are user-controlled (the user selects which prompts to execute).
Since its launch, MCP has seen rapid adoption. Not only Anthropic, but also Microsoft and, interestingly enough, Google's Agent Development Kit (ADK) supports MCP. The open nature of the protocol has contributed to its growing ecosystem of tools and integrations.
But while MCP excels at connecting individual AI applications to tools and data, it doesn't define how multiple AI agents should talk to each other - and that's where A2A enters the picture.
Understanding A2A from the Foundation
While MCP was solving the problem of connecting AI models to tools and data, a different challenge was emerging: how do autonomous AI agents communicate with one another, especially when built by different vendors on different frameworks?
This is not a theoretical problem. Multi-agent systems have been around for a while, with frameworks like LangChain, CrewAI, and AutoGen already providing ways for agents to collaborate. But each framework had its own approach to agent communication, creating interoperability challenges.
Google's new Agent2Agent (A2A) Protocol aims to address this by providing a standardized way for agents to collaborate, regardless of the underlying framework or vendor. It provides a common language for these AI agents to communicate. It's built on familiar technologies like HTTP, SSE (Server-Sent Events), and JSON-RPC, making it easier to integrate with existing IT infrastructure.
The A2A protocol facilitates communication between what Google calls a "client" agent and a "remote" agent. In simpler terms, a client agent creates tasks and communicates them to the remote agent, expecting some work to be performed or data returned.
A2A introduces several key capabilities:
Capability Discovery: Agents expose their capabilities through "Agent Cards" in JSON format, allowing other agents to identify the best agent for a particular task.
Task Management: The protocol defines how agents handle tasks that can be completed immediately or be long-running, with mechanisms to stay in sync.
Collaboration: Agents can send each other messages to communicate context, replies, artifacts, or user instructions.
User Experience Negotiation: This interesting feature allows agents to negotiate the format in which data should be returned to fit the user's UI expectations.
One of A2A's design principles is "opaque execution" - agents don't have to share their internal memory, plans, or tools, only the inputs and outputs matter. This is a major departure from how many multi-agent systems have been built so far, where agents often have access to each other's internal states.
Google has already secured support from over 50 partners including major firms like Salesforce, ServiceNow, and Workday. Notably absent from this list? Anthropic and OpenAI.
MCP and A2A - Complementary or Competing?
Google has positioned A2A as complementary to MCP, stating "A2A is an open protocol that complements Anthropic's Model Context Protocol (MCP), which provides helpful tools and context to agents." But is this truly the case, or is there more nuance to the relationship?
The Official Position
Google's documentation presents A2A and MCP as solving different parts of the AI communication puzzle. According to their A2A documentation: "Agentic applications need both A2A and MCP. We recommend MCP for tools and A2A for agents."
Their auto repair shop analogy illustrates this division of labor: MCP connects agents with structured tools (like socket wrenches), while A2A enables the mechanics to talk to each other about complex problems ("my car is making a rattling noise").
In this model, MCP handles vertical integration (application-to-model), while A2A provides horizontal integration (agent-to-agent). Each protocol stays in its lane, solving distinct problems:
MCP's Domain: Standardizing how LLMs access tools, data, and context
A2A's Domain: Standardizing how agents communicate and coordinate with each other
The Vertical vs. Horizontal Integration Model
Let's break this down with a concrete example of how these protocols might work together in a company setting:
Imagine you're building an AI system to help manage your company's hiring process:
You'd use MCP to connect your AI agents to:
Your HR database (via a PostgreSQL MCP server)
Company documents and policies (via a filesystem MCP server)
Your ATS (Applicant Tracking System) through a custom MCP server
You'd use A2A for communication between specialized agents:
A resume screening agent
A job requirements analysis agent
An interview scheduling agent
A candidate communication agent
Each agent uses MCP to access the tools and data it needs, while using A2A to coordinate with other agents on complex tasks like "Find suitable candidates for the Senior Developer role."
This neat division sounds logical, but is it truly how these protocols will develop in the real world?
A Skeptical View
There can be a significant overlap between these protocols. There are several reasons to question if the "complementary" relationship will hold:
1. Blurring Lines Between Tools and Agents
The distinction between "tools" and "agents" is increasingly fuzzy. Modern tools often have agent-like properties, such as:
Maintaining state
Making autonomous decisions
Having specialized capabilities
Adapting to different contexts
Similarly, agents often function as tools for other agents. In multi-agent architectures like those found in LangChain or CrewAI, agents regularly call other agents as if they were tools.
2. Expanding Protocol Scopes
Both protocols are likely to expand their capabilities over time:
MCP could add more robust agent communication features
A2A could incorporate more tool-like integrations
History shows that successful protocols tend to grow beyond their initial scope. HTTP was originally just for document retrieval but now powers complex applications. REST was for simple data access but evolved to support complex workflows.
3. Developer Attention is Limited
Developers face limited time and cognitive bandwidth. Learning, implementing, and maintaining two separate protocols creates overhead. There's a natural pressure to consolidate on fewer standards over time.
4. Competing Corporate Interests
While Google claims A2A complements MCP, it's worth noting the competitive dynamics at play:
Google released A2A shortly after OpenAI adopted MCP
Google CEO Sundar Pichai publicly questioned "to MCP or not to MCP?" before releasing A2A
Anthropic and OpenAI are notably absent from A2A's launch partners
While the protocols may be complementary in theory, there's a strategic dimension to their development and promotion.
Technical Comparison Points
Looking at the technical details reveals both similarities and differences that could influence how these protocols compete or complement each other:
1. Authentication and Security
A2A was designed with security as a core principle, supporting enterprise-grade authentication and authorization from the start. MCP initially lacked robust authentication mechanisms, though this has improved in recent updates.
2. State Management
A2A explicitly supports stateful, long-running tasks with mechanisms for agents to stay synchronized. MCP was primarily designed for stateless interactions, though stateful patterns can be implemented.
3. Discovery Mechanisms
Both protocols implement discovery mechanisms, but in different ways:
MCP focuses on discovering tools, resources, and prompts
A2A focuses on discovering agent capabilities through Agent Cards
4. Communication Patterns
A2A supports rich communication patterns including task negotiation, long-running operations, and multimodal exchanges. MCP's communication is more focused on resource access and tool invocation.
The Developer's Dilemma
For developers building AI systems today, this competition or complementarity creates real questions:
Should you invest in learning both protocols?
Should you design your architecture assuming both will coexist?
Should you bet on one protocol becoming dominant?
Should you adopt a "wait and see" approach to avoid rework?
There's no one-size-fits-all answer, as it depends on your specific use cases, risk tolerance, and development timeline.
The Reality of Protocol Evolution
History teaches us that protocol evolution rarely follows a neat, planned path. Instead, it tends to be messy, with competing standards, partial adoptions, and eventual consolidation.
Remember browser wars? Java vs. .NET? SOAP vs. REST? In each case, what seemed like a clear technical distinction often became blurred by market dynamics, developer preferences, and the natural evolution of the technologies.
The same is likely true for MCP and A2A. While they start with distinct purposes, their future relationship will be shaped by:
Which protocol delivers the most developer value
Which gains the strongest ecosystem support
Which adapts more quickly to emerging needs
Which strikes the better balance between power and simplicity
A Typical AI Pipeline with Both Protocols
In a typical AI agent pipeline today, here's how these protocols might fit:
User Interaction Layer: Where the user interacts with your AI system
Agent Orchestration Layer: Where multiple agents coordinate to solve problems (A2A territory)
Tool Integration Layer: Where agents connect to external tools and data sources (MCP territory)
This separation works well in theory, but in practice, the boundaries may blur. An agent at layer 2 might be a tool at layer 3 for another system. The orchestration layer might need direct access to tools without going through agents.
What Prevails in the Future
As AI systems grow more complex, we're moving from single monolithic agents to ecosystems of specialized agents working together. This raises important questions about how these agent ecosystems should be architected.
Evolving Architectures
The emergence of protocols like MCP and A2A reflects a broader trend toward modularity in AI systems. Rather than building one-off integrations or proprietary communication methods, developers are seeking standardized ways to connect components.
Several potential architectural patterns are emerging:
Hierarchical Agent Systems: Where a primary agent delegates to specialized sub-agents via A2A, each connecting to tools via MCP
Peer-to-Peer Agent Networks: Where equal agents discover and communicate with each other as needed, potentially using A2A for communication
Tool-First Architectures: Where the focus is on building powerful tool integrations via MCP, with agent interactions as a secondary consideration
Agent-as-Platform: Where companies expose their capabilities as agents rather than as tools or APIs, potentially favoring A2A
What Matters for Developers
For developers building AI systems today, a few principles can help navigate this evolving landscape:
Start with Use Cases: Choose protocols based on your specific needs rather than hype
Embrace Standards: Standardized approaches will generally serve you better than custom solutions
Stay Flexible: Design your architecture to adapt as these protocols evolve
Watch Adoption: The value of a protocol increases with its ecosystem
Prefer Simplicity: When in doubt, the simpler solution often wins in the long run
What's clear is that we're in the early days of defining how AI agents will communicate and coordinate. The protocols that win will be those that strike the right balance between power and simplicity, while building strong ecosystems of developers and tools.
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