A2A vs MCP
Comparing Google's A2A protocol with Anthropic's MCP protocol, understanding their differences and complementary nature
Contents
Overview
In the evolution of AI agent technology, two important protocols play key roles: Google's Agent2Agent (A2A) Protocol and Anthropic's Model Context Protocol (MCP). While both protocols relate to AI agents, they address different layers of problems in agent architecture.
A2A ❤️ MCP: As stated in Google's official documentation, these protocols are not competing but complementary. They solve different yet equally important problems in AI agent systems.
A2A Protocol
An open protocol by Google that enables communication and interoperability between opaque agentic applications. A2A provides a standardized way for agents to discover each other's capabilities and collaborate securely across different frameworks and vendors.
Extends wide-area collaboration between AI agents
MCP Protocol
An open protocol developed by Anthropic that, according to their official documentation, "standardizes how applications provide context to LLMs." Think of MCP like a USB-C port for AI applications, connecting AI models to different data sources and tools.
Enhances the depth capabilities of single AI agents
Detailed Comparison
Feature | A2A Protocol | MCP Protocol |
---|---|---|
Developed By | Anthropic | |
Core Objective | Enable interoperability and collaboration between different AI agents | Enhance capabilities and context management of individual AI agents |
Scope | External communication (between agents) | Internal processing (between application and LLM) |
Key Functions | Agent discovery via Agent Cards, task-based workflow, streaming updates, push notifications, rich content support | Tool access, context management, structured data handling, file system integration, desktop application support |
Communication Direction | Horizontal communication (across agents) | Vertical communication (application-LLM) |
Technical Foundation | HTTP, Server-Sent Events (SSE), JSON Schema, Agent Cards, Task-based workflow | JSON structures, standardized interfaces, tool specifications, context providers |
Key Challenges | Cross-agent interoperability, secure communication between different vendors, task coordination, agent discovery | Standardized context management, tool integration, structured data handling, file system access |
Note: This comparison is based on publicly available information from official documentation and may evolve as these protocols develop.
Key Differences
Focus Area
A2A focuses on inter-agent communication, while MCP focuses on agent-LLM integration.
Problem Solving
A2A solves the problem of agent collaboration, while MCP solves the problem of agent capability enhancement.
Implementation Level
A2A operates at the system integration level, while MCP operates at the application architecture level.
Complementary Nature
A2A and MCP protocols are complementary by design, together forming a complete AI agent capability system. Here are the key aspects of their complementary nature:
Complementary Architecture
MCP standardizes how applications provide context to LLMs (like a USB-C port for AI), while A2A standardizes how agents communicate with each other. Together they address both internal agent capabilities and external agent collaboration.
Ecosystem Coexistence
Enterprises can use MCP to enhance individual agents with better context and tool access, then use A2A to enable these agents to collaborate across organizational boundaries, regardless of which frameworks or vendors they're built on.
Open Standards Approach
Both protocols are open standards, encouraging community contributions and ecosystem growth. This open approach helps prevent vendor lock-in and enables enterprises to build more flexible, interoperable AI agent systems.
A2A ❤️ MCP
A2A ❤️ MCP
Google's A2A documentation explicitly mentions MCP, stating: "A2A and MCP are not competing but complementary protocols." This indicates that both protocols are designed to work together, with MCP focusing on enhancing individual agent capabilities through better context management, while A2A enables these enhanced agents to collaborate effectively.
Together, these protocols create a complete solution for AI agent systems
Collaborative Workflow
In practical applications, MCP and A2A can work together to form a complete multi-agent system. Here's an example of how they collaborate:
Personal Assistant Agent System Example
Imagine a Personal Assistant Agent System (PAAS) that needs to interact with an external Restaurant Reservation Agent System (RRAS).
Internal Processing (MCP)
PAAS uses MCP to manage its internal "thinking process," understanding the user request ("book a restaurant for tomorrow at 7 PM") and deciding to contact an external restaurant reservation service.
External Communication (A2A)
PAAS's orchestrator uses the A2A protocol to communicate with RRAS, sending a structured reservation request with date, time, party size, etc.
Internal Processing (MCP)
RRAS uses MCP to interact with its internal LLM, processing the reservation request and checking availability.
External Communication (A2A)
RRAS sends available time options back to PAAS via the A2A protocol.
Internal Processing (MCP)
PAAS uses MCP to present these options to the user and process the user's selection.
External Communication (A2A)
PAAS sends the final confirmation to RRAS via the A2A protocol.
Bridging Role
Bridging Role
In this collaborative model, the agent's Orchestrator plays a crucial bridging role:
MCP Integration
It uses MCP to interact with the internal LLM, obtaining intentions and decisions
A2A Communication
It uses A2A to communicate with external agent systems, exchanging information and requests
Protocol Translation
It's responsible for translating between these two protocols, ensuring proper information flow
// Orchestrator component bridging MCP and A2A
class AgentOrchestrator {
async processUserRequest(request) {
// Use MCP to process the request internally
const intent = await this.mcpClient.processContext({
userRequest: request,
tools: this.availableTools
});
if (intent.requiresExternalAgent) {
// Use A2A to communicate with external agent
const externalAgent = await this.a2aClient.discoverAgent(
intent.requiredCapability
);
const task = await this.a2aClient.createTask({
agentId: externalAgent.id,
request: intent.formattedRequest
});
// Handle the response from external agent
return this.handleExternalResponse(task);
}
return intent.response;
}
}
Real-World Use Cases
Here are several real-world use cases where A2A and MCP work together, demonstrating how they collectively create value for enterprises:
Enterprise Customer Service
MCP Application
Customer service agents use MCP to access customer history, product databases, and knowledge bases to provide personalized service.
A2A Application
When customer issues require specialized knowledge, the customer service agent collaborates with department-specific agents via A2A, enabling seamless handoffs and context sharing.
Combined Value
Customers receive a one-stop service experience without needing to repeat their issues, while enterprises can integrate expertise from different departments.
Medical Diagnosis
MCP Application
Diagnostic agents use MCP to access medical literature, patient records, and diagnostic tools to form preliminary diagnoses.
A2A Application
The diagnostic agent collaborates with specialist agents, medication recommendation agents, and treatment planning agents via A2A to develop comprehensive treatment plans.
Combined Value
Patients receive more comprehensive, accurate diagnoses and personalized treatment plans, while medical resources are used more effectively.
Supply Chain Management
MCP Application
Inventory management agents use MCP to access inventory data, sales forecasts, and historical patterns to optimize inventory levels.
A2A Application
Inventory agents collaborate with supplier agents, logistics agents, and finance agents via A2A to coordinate ordering, shipping, and payment processes.
Combined Value
Enterprises achieve end-to-end supply chain automation, reducing delays and errors while improving capital efficiency.
Integration Benefits
Enhanced Capabilities
Agents with MCP have better context and tool access, making A2A collaborations more effective
Vendor Independence
Organizations can use best-of-breed agents from different vendors that work together seamlessly
Scalable Architecture
Systems can grow organically by adding new specialized agents that integrate with existing ones
ROI Optimization
Enterprises can maximize their AI investments by enabling cross-functional agent collaboration