Agent-to-Agent (A2A) and Model Context Protocol (MCP) are two of the most widely used AI protocols that have garnered significant attention as of recent. At first glance, one might think “A2A vs MCP” as an either/or choice, but in reality these protocols address different challenges. This article elucidates what A2A and MCP are, clarifies their distinct roles in AI systems, and explains how they complement each other to enable integration across enterprise AI workflows.
Table of contents
What is A2A (Agent-to-Agent)?
Agent2Agent (A2A) is an open protocol by Google that standardizes how AI agents communicate and collaborate. Essentially, A2A allows independent AI agents built by different vendors or running on different platforms to form a common language for cooperation. Using A2A, agents can exchange goals, share context, and invoke actions with each other in a secure, structured manner. The protocol was explicitly designed to allow multi-agent workflows that span across different clouds, applications, or services. A2A is built on familiar web standards such as HTTP, making it easier to integrate it into existing IT stacks.
To learn about the workings of the A2A protocol, refer to this article: How A2A works?
What is MCP (Model Context Protocol)?
The Model Context Protocol (MCP) was introduced by Anthropic (parent company of Claude), which allows connecting AI agents (or LLMs) to external tools. If A2A is about agent-to-agent communication, MCP is about agent-to-resource integration. It provides a unified, standardized way for AI models to access various data sources, knowledge bases, and services that are outside the model’s own parameters. That is why it is commonly referred to as the “USB-C port” for AI applications. Prior to it, developers had to write custom integrations for each new tool or data source (leading to a tangle of one-off connectors). MCP replaces that with one open protocol so that any compliant data/service connector can work with any MCP-aware agent.
To learn about the workings of MCP, refer to this article: How MCP works?
For a video covering the MCP protocol, refer to this:
A2A vs MCP
This table summarizes the differentiated roles of A2A vs MCP:
| Aspect | A2A (Agent-to-Agent) | MCP (Model Context Protocol) |
|---|---|---|
| Purpose | Connects and coordinates multiple agents (agent ↔ agent) | Connects agents to external tools/data (agent ↔ resource) |
| Key Functionality | Task delegation between agents; context and goal exchange | Tool and data integration; provides real-time context to agents |
| Created by | Google (open spec with partners contributing) | Anthropic (open spec with multi-vendor adoption) |
| Ecosystem Support | Microsoft (Azure AI Foundry, Copilot Studio), Google, Atlassian, Salesforce, ServiceNow, etc. | Microsoft (Copilot Studio), Google, OpenAI, Anthropic (Claude), Atlassian, etc. |
| Focuses On | Inter-agent communication: security, trust, and interoperability when agents collaborate. | Agent extensibility: uniform access to data sources and tools, maintaining up-to-date context for the agent. |
| Analogy | Protocol for conversation and teamwork between AI agents. | Universal plug for connecting an AI to any data/tool it needs. |
Key Differences
A2A and MCP operate in distinct domains of AI architecture. Here’s a concise breakdown of the 3 main differences between them:
- Scope of Interaction: A2A connects agents to each other. On the other hand, MCP connects agents to external tools and data. Google positions A2A as a standard enabling agent collaboration, while Claude’s MCP focuses on bridging agents with external services.
- Primary Function: A2A handles communication, task delegation, and state sharing between agents. MCP equips individual agents with functionality by connecting them to external resources via a unified, tool-based interface.
- Design Principles: A2A is built on HTTP/JSON standards and supports agent discovery and secure delegation. MCP uses JSON-RPC and emphasizes tool registration, data access, and real-time context feeding. A2A sees agents as peers, and MCP sees tools as callable services.
How They Work Independently
A2A Alone: Picture a company with specialized AI agents in domains such as finance, marketing, and scheduling. A master agent can delegate tasks like budgeting or timeline planning to others using A2A. Each agent contributes results back through a shared protocol. Without MCP, though, each agent relies only on its internal knowledge or hardwired connections.
MCP Alone: Imagine a support chatbot connected to live systems such as product databases, shipping APIs, and knowledge bases using MCP. This setup makes the agent dynamically aware and actionable in real time. Even without A2A, MCP turns it into a tool-rich, responsive assistant. However, it can’t coordinate across multiple agents to solve complex or multi-step problems.
Independently, both protocols bring clear value. A2A enables modular teamwork, whereas MCP allows agents to have external functionality.
Integration (Better Together)
In modern GenAI systems, A2A and MCP often operate together to enable intelligent orchestration:
- Layered Cooperation: Think of MCP as the foundation for tools and data access and A2A as the coordination layer that delegates tasks among agents. In a supply chain example, agents fetch inventory data, handle procurement, and manage delivery using MCP, while A2A enables them to share tasks and results.
- Unified Development Experience: Microsoft Copilot Studio showcases this integration. Developers can register MCP tools and link agent workflows via A2A, all in one interface. A2A handles the flow, and MCP handles the function.
Misconceptions
Despite their origins in different orgs, A2A vs. MCP shouldn’t exist as they are not competing standards:
- Different Problems: A2A is for communication, whereas MCP is for execution. They operate on separate protocol layers.
- Complementary Functions: A2A enables task-sharing between agents. MCP lets each agent use tools.
- Industry-Wide Alignment: Microsoft integrates A2A in Copilot and registers MCP tools. Anthropic open-sourced MCP and backs A2A adoption.
- No Hierarchy of Importance: Both solve crucial challenges. A2A without MCP leads to clueless agents; MCP without A2A creates isolated agents.
The owners of both the standards (Google and Anthropic) are actively trying to encourage integration of both the standards, in enterprise AI workflows. Using both means building agentic systems that are capable of adapting and scaling.
Complementary Strengths
The two protocols excel at handling a specific workflow. But when used together, they make up for each other:
- Interoperability + Extensibility: A2A connects agents across systems. MCP makes each agent extensible. Together, they create modular, flexible ecosystems.
- Specialization + Cooperation: Agents can specialize, and still collaborate. MCP gives them the tools, while A2A allows them to share the workload.
- Real-Time Adaptation: MCP delivers fresh context, whereas A2A reroutes tasks if conditions change. Systems become resilient and responsive.
- Governance + Observability: MCP governs tool access, while A2A governs interactions. Together, they offer traceability, compliance, and control.
Together, they bring intelligence and interoperability to generative AI systems.
Conclusion
A2A and MCP are not silos, they’re synergistic standards. Each solves a separate problem. But, when combined, they empower agents to communicate (A2A) and act with real-world context (MCP).
Microsoft CEO Satya Nadella said it best:
“Open protocols like A2A and MCP are key to enabling the agentic web… [so] customers can build agentic systems that interoperate by design.”
The future of GenAI isn’t about picking one protocol over another. It’s about finding ways of entwining them for our workflows. Together, they lay the foundation for next-gen intelligent systems which are interoperable and tool-aware.
Frequently Asked Questions
Q1. What’s the difference between A2A and MCP in AI systems?
A. A2A connects multiple AI agents to communicate and delegate tasks, while MCP connects an agent to tools and data sources for real-world functionality.
Q2. Can A2A and MCP be used together in the same system?
A. Yes, they are designed to complement each other. A2A handles coordination between agents, and MCP provides tool and data access.
Q3. Who created A2A and MCP, and are they open standards?
A. A2A was developed by Google, MCP by Anthropic, and both are open protocols adopted by companies like Microsoft and OpenAI.
I specialize in reviewing and refining AI-driven research, technical documentation, and content related to emerging AI technologies. My experience spans AI model training, data analysis, and information retrieval, allowing me to craft content that is both technically accurate and accessible.
