MCP vs A2A: Competitors or Complements?
- Philip Moses
- May 17
- 4 min read
Updated: May 22
As AI systems evolve from standalone models into collaborative, task-oriented agents, the need for communication standards has never been more pressing. Two emerging protocols—Anthropic’s MCP (Model Context Protocol) and Google’s A2A (Agent-to-Agent)—are stepping up to define how intelligent agents interact with tools, data, and each other. But with two powerful players backing different approaches, the question arises: Are these protocols competing for dominance, or are they part of a larger, complementary vision for the future of AI?
In this blog, we’ll explore:
What A2A and MCP actually do
How they differ in purpose and implementation
Whether they can coexist or are headed for a protocol clash
What this means for developers and the broader AI ecosystem
Let’s dive in.
A2A: Google's Vision for Agent-to-Agent Communication
Google’s recently introduced A2A protocol is designed to streamline communication between autonomous AI agents. True to its name, A2A focuses on standardizing how agents discover and interact with each other—regardless of who built them or what ecosystem they belong to.
A2A enables agents to:
Securely exchange information
Collaborate dynamically across enterprise systems and applications
Coordinate tasks in real time
How A2A Works
Agents using A2A expose a public-facing profile—called a "card"—that includes:
Location and version info
Capabilities (or skills) the agent supports
Protocols supported for communication (e.g., HTTP, SSE, push notifications)
Depending on the nature of the task, A2A supports different methods of interaction:
Request/response with polling for standard tasks
Server-Sent Events (SSE) for short, real-time updates
Push notifications for long-running background processes
By focusing on structured agent-to-agent communication, Google aims to support scalable, cooperative AI ecosystems that can operate across silos.
MCP: Anthropic’s Approach to Context Integration
Anthropic’s Model Context Protocol (MCP) tackles a different challenge—enabling language models and AI agents to securely connect with external data and tools. MCP is all about providing intelligent agents with context, which is essential for producing relevant, accurate, and insightful responses.
MCP connects models to:
Files and databases
APIs and business tools
IDEs and development environments
How MCP Works
MCP follows a client-server model, enabling applications (clients) to interact with tools (servers) through a standardized protocol. This includes:
MCP Clients like chatbots or desktop apps
MCP Servers which represent tools or data sources
Local and remote services that supply contextual input
By providing a unified structure for accessing and interpreting external data, MCP makes it easier to build powerful agent workflows that adapt to real-world environments.
Different Purposes, Shared Potential
At first glance, A2A and MCP seem to address entirely separate layers of the AI stack:
MCP focuses on contextualizing models through external data
A2A centers on coordinating communication between autonomous agents
This distinction is what Google emphasizes—claiming A2A is not a competitor, but a complement to MCP. For example, in a smart auto repair use case:
MCP would enable access to repair manuals or diagnostic tools
A2A would facilitate communication between customer service bots, repair agents, and inventory systems
However, as systems become more complex, these boundaries blur. Tools are becoming more autonomous, and agents are increasingly acting as orchestrators. This raises a natural question...
Competition or Collaboration? Industry Voices Weigh In
While the official stance is complementary, some experts suggest there could be friction. Developers and organizations can only dedicate so much time and effort to integrating protocols—forcing them to choose between ecosystems.
“In theory they can coexist. In practice, I foresee a tug of war.”— Solomon Hykes, CEO of Dagger (ex-Docker).
The crux of the challenge lies in adoption. Protocols don’t just set technical standards—they shape which tools thrive, who builds what, and how fast innovation scales.
Why Did Google Launch A2A Now?
Google’s push for A2A comes just after OpenAI publicly adopted MCP, and Anthropic’s MCP continues gaining traction. Instead of choosing sides, Google appears to be hedging its bets:
Publicly supporting MCP, promising to integrate it into Gemini models
Simultaneously launching A2A, building its own vision of agent collaboration
This strategy allows Google to both support the community standard and advocate for its own protocol that aligns with broader multi-agent ambitions.
Lessons from the Past: Simplicity Wins
Tech history teaches us that ease of use and community support often determine a standard’s success. In the web era, SOAP and XML lost to JSON—not because they weren’t powerful, but because they were harder to adopt.
The same principle may apply here. The winning protocol will likely be the one that:
Has the simplest implementation path
Attracts a strong developer ecosystem
Gains momentum through real-world use cases
Conclusion: A2A and MCP in the Future of AI
So, are MCP and A2A rivals or allies? The answer might be: both.
While each protocol targets a different problem—context vs. communication—they both aim to support the same goal: a future where intelligent agents can act, learn, and collaborate in powerful ecosystems.
Their coexistence will depend on how clearly they maintain their separate roles—and how seamlessly they can integrate with one another in real-world AI applications.
As the industry pushes forward, one thing is certain: The protocol—or protocols—that simplify development and accelerate adoption will lead the charge.
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