Agent-to-Agent Protocols: A2A and MCP

Google's Agent2Agent Protocol (A2A)

The Agent2Agent Protocol (A2A) is an open protocol announced by Google in April 2025, designed to enable seamless communication and interoperability between AI agents, regardless of the underlying framework or vendor.

A2A Protocol: Enabling agent-to-agent communication

The Problem A2A Solves

Enterprise AI adoption faces a major hurdle: getting agents built on different frameworks and vendors to work together effectively. A2A creates a standardized way for these diverse agents to communicate and collaborate.

The A2A Solution

A2A provides a common language for agents to discover each other's capabilities, negotiate user experience elements, and work together securely, regardless of which framework or vendor they were built on.

                    A2A Design Principles
                    
                            Embrace agentic capabilities: Focus on enabling natural collaborative workflows
                        
                            Build on existing standards: Uses HTTP, SSE, JSON-RPC
                        
                            Secure by default: Enterprise-grade authentication and authorization
                        
                            Support for long-running tasks: Real-time feedback and state updates
                        
                            Modality agnostic: Supports various formats including audio and video

Key Components of A2A

Agent Card

A public metadata file that describes an agent's capabilities, skills, endpoint URL, and authentication requirements. Used for agent discovery.

Task Management

The communication between agents is task-oriented, with a defined lifecycle. Tasks can be completed immediately or remain active for extended periods.

UX Negotiation

Agents can negotiate the correct format for content, explicitly including negotiations about user interface capabilities.

A2A: Enabling communication between opaque agentic applications

With support from over 50 technology and service partners including Atlassian, Box, Cohere, Intuit, Langchain, MongoDB, PayPal, Salesforce, SAP, and ServiceNow, A2A is positioned to become a crucial standard for agent interoperability in enterprise environments.

Anthropic's Model Context Protocol (MCP)

The Model Context Protocol (MCP) is an open standard introduced by Anthropic that standardizes how AI applications connect with external tools, data sources, and systems. It enables AI assistants to access and interact with data where it lives, breaking down information silos.

Visual Guide to Model Context Protocol

The Problem MCP Solves

Even advanced AI models are constrained by isolation from data trapped in silos and legacy systems. Each data integration requires custom implementation, making connected systems difficult to scale.

The MCP Solution

MCP provides a universal standard for connecting AI systems with data sources, replacing fragmented integrations with a single protocol for simpler, more reliable data access.

MCP Architecture

MCP follows a client-server architecture with three primary components:

MCP Host

Applications like Claude Desktop, IDEs, or any AI-powered tool that wants to access data through the MCP protocol. The host is the entry point for user interactions.

MCP Client

Protocol clients that maintain 1:1 connections with servers. They handle the communication between hosts and servers, managing the protocol details.

MCP Server

Lightweight programs that each expose specific capabilities through the standardized protocol. They connect to the actual data sources and tools.

Overview of MCP connectivity

                    Key Characteristics of MCP
                    
                            Secure: Operates within your security boundaries
                        
                            Open: Community-driven open source standard
                        
                            Interoperable: Works across LLM providers
                        
                            Extensible: Adaptable to various data sources

Animated visualization of MCP data flow

Unlike proprietary integration methods, MCP is designed as a collaborative, open-source ecosystem that any developer or organization can contribute to and benefit from. It focuses on providing AI models with access to tools and data they need to be effective.

A2A vs MCP: Complementary Protocols

While both A2A and MCP are open protocols in the AI agent ecosystem, they serve different yet complementary purposes. Understanding their similarities and differences is key to implementing the right solution for your needs.

Comparison of A2A and MCP architectures and purposes

Feature	A2A (Google)	MCP (Anthropic)
Primary Purpose	Enable communication between different AI agents	Connect AI models to data sources and tools
Focus	Agent-to-agent collaboration	Agent-to-data/tool connectivity
Communication Pattern	Task-based with messages between agents	Request-response for accessing tools and data
Key Components	Agent Card, Tasks, Messages, Parts, Artifacts	Hosts, Clients, Servers, Tools, Resources
Discovery Mechanism	Agent Card and capability advertising	Host discovers available servers
Transport	HTTP, SSE, JSON-RPC	Stdio, HTTP/SSE, Websockets
Ideal Use Case	Multi-agent workflows requiring collaboration	Single agent needing access to external systems
Announced	April 2025	November 2024

How They Complement Each Other

MCP and A2A address different but related challenges in the AI ecosystem:

MCP solves the critical challenge of connecting AI models to the tools, data, and systems they need to be useful.

A2A addresses the equally important challenge of enabling different agents to communicate and collaborate effectively.

How A2A and MCP can work together in an ecosystem

Think of MCP as the protocol to connect agents with their structured tools (e.g. "search this database", "read this file"), while A2A is the protocol that enables end-users or other agents to work with specialized agents ("find candidates for this job posting", "summarize my meeting notes").

How These Protocols Work

Both A2A and MCP operate through structured workflows that allow for standardized communication patterns, though they serve different purposes.

A2A Workflow

1

Agent Discovery

A client agent discovers remote agents by accessing their Agent Cards, which describe their capabilities and endpoints.

2

Task Initialization

The client agent sends a task request to a remote agent, including the initial message and task parameters.

3

Task Processing

The remote agent processes the task, which can either complete immediately or enter a working state for longer-running operations.

4

Status Updates

For long-running tasks, the remote agent sends status updates to the client agent using Server-Sent Events (SSE).

5

Message Exchange

Agents can exchange messages containing text, files, or structured data as "parts" to collaborate on the task.

6

Task Completion

The task reaches a terminal state (completed, failed, or canceled) and may return artifacts as the result.

MCP Workflow

1

Connection Initialization

The MCP Client in the host application establishes a connection with an MCP Server, exchanging capabilities and protocol versions.

2

User Query

A user interacts with the host application (like Claude Desktop), and the AI model identifies a need for external data.

3

Resource Request

The MCP Client sends a request to the appropriate MCP Server to access needed data or tools.

4

Server Processing

The MCP Server processes the request by accessing the requested resource (file, database, API, etc.) and formats the data.

5

Response Delivery

The MCP Server returns the formatted data to the MCP Client following the standardized protocol.

6

AI Response Generation

The AI model in the host application uses the retrieved data to generate a more informed response for the user.

Demonstration of A2A in action

Example of MCP in action with Claude

Key Protocol Features Comparison
                            A2A Key Features
                            Agent discovery through Agent Cards
Task-based communication
Support for long-running operations
User experience negotiation
Multi-modal content (text, files, data, audio, video)

                        

                            MCP Key Features
                            Server discovery and capability exploration
Standardized resource access
Tool invocation with parameters
Multiple transport options
Consistent error handling

                        

The standardized nature of these protocols means that AI models and agents don't need to understand the specific details of each data source or communication partner — they only need to know how to interact with the protocol itself.

Implementation Examples

Both A2A and MCP can be implemented in various ways depending on your use case. Below are examples of how these protocols can be implemented in real-world scenarios.

A2A Implementation Examples

Sample A2A Agent Card (JSON)

{
  "name": "Recruitment Assistant",
  "description": "An agent that helps find and evaluate job candidates",
  "version": "1.0.0",
  "url": "https://recruitment-agent.example.com/a2a",
  "authentication": {
    "type": "oauth2",
    "authorizationUrl": "https://auth.example.com/authorize",
    "tokenUrl": "https://auth.example.com/token",
    "scopes": ["read", "write"]
  },
  "capabilities": {
    "streaming": true,
    "multimodal": true
  },
  "skills": [
    {
      "name": "findCandidates",
      "description": "Find candidates matching job requirements",
      "parameters": {
        "jobTitle": {
          "type": "string",
          "description": "The job title to search for"
        },
        "skills": {
          "type": "array",
          "description": "Required skills for the position"
        },
        "location": {
          "type": "string",
          "description": "Preferred location for the candidate"
        }
      }
    }
  ]
}

Candidate Sourcing Scenario

A recruiting manager needs to find candidates for a software engineering position:

The manager's agent connects to specialized recruiting agents
Each agent handles a specific part of the process (sourcing, screening, scheduling)
The agents collaborate using A2A to find suitable candidates
The manager receives a unified view of the results
After selecting candidates, the manager's agent uses A2A to coordinate interview scheduling

Multi-Agent Research Scenario

A data scientist needs comprehensive research on a complex topic:

The primary agent divides research tasks among specialized agents
A market research agent gathers industry trends
A data analysis agent processes relevant statistics
A visualization agent creates charts from the processed data
The primary agent synthesizes all information into a cohesive report

A2A implementation showing agent communication flow

MCP Implementation Examples

Sample MCP Server Implementation (Python)

from mcp.server import MCPServer
from mcp.schema import Capability, Action

# Create a simple MCP server
server = MCPServer(name="FileReader",
                   description="Reads files from the local filesystem")

# Define a capability
file_capability = Capability(
    name="read_file",
    description="Reads content from a file",
    parameters={
        "path": {
            "type": "string",
            "description": "Path to the file"
        }
    },
    returns={
        "content": {
            "type": "string",
            "description": "Content of the file"
        }
    }
)

# Implement the capability
@server.implement(file_capability)
def read_file(path):
    try:
        with open(path, 'r') as f:
            content = f.read()
        return {"content": content}
    except Exception as e:
        return {"error": str(e)}

# Start the server
if __name__ == "__main__":
    server.start()

Document Analysis Scenario

A lawyer needs to analyze a set of legal documents:

Claude identifies the need to access local files
The MCP Host connects to a FileReader MCP Server
The server securely accesses the documents
Claude receives the document contents in a standardized format
Claude analyzes the legal language and provides insights

Integrated Development Scenario

A developer uses an AI coding assistant in their IDE:

The AI assistant needs to understand the codebase context
MCP connects the assistant to Git and GitHub MCP Servers
The Git server provides local repository information
The GitHub server provides issue and PR context
The assistant generates contextually appropriate code

Detailed example of MCP implementation flow

Available Implementations

A2A Implementations

Sample A2A Client/Server (Python, JavaScript)
Multi-Agent Web Applications
Command-line interfaces
Integration with frameworks like:
- Agent Developer Kit (ADK)
- CrewAI
- LangGraph
- Genkit

MCP Servers

Pre-built MCP servers for popular services:

GitHub

Slack

Google Drive

PostgreSQL

Git

Puppeteer

Use Cases

A2A and MCP enable a wide range of powerful use cases across different industries and scenarios. Here are some examples of how these protocols are being used in real-world applications.

Software Development

AI coding assistants can access repositories, documentation, and codebase context to provide more accurate suggestions and explanations.

Code analysis with repository context (MCP)

Collaborative code review (A2A)

Multi-agent debugging workflows (A2A)

Data Analysis

Business analysts can use AI tools that access databases and spreadsheets to analyze data, create reports, and generate insights.

Query data from various sources (MCP)

Collaborative data exploration (A2A)

Multi-agent reporting workflows (A2A)

Enterprise Workflows

Connect various enterprise systems to enable complex workflows across departments and applications.

Access to enterprise data sources (MCP)

Cross-department agent collaboration (A2A)

Secure information sharing (Both)

Healthcare

Medical professionals can use AI assistants that access patient records and medical literature while maintaining privacy compliance.

Secure access to medical records (MCP)

Specialized diagnostic collaboration (A2A)

Patient history summarization (MCP)

Education

Students and educators can use AI tools that access learning materials, assignments, and research papers to enhance the learning experience.

Access to educational resources (MCP)

Collaborative tutoring agents (A2A)

Personalized learning experiences (Both)

Building AI teams with A2A and MCP protocols

Real-World Applications

Multi-agent Customer Support

Customer support teams use specialized agents for different parts of the support process: inquiry routing, knowledge base searching, ticket creation, and follow-up coordination.

Utilizes both A2A for agent communication and MCP for data access

Supply Chain Optimization

AI agents coordinate across inventory management, logistics planning, demand forecasting, and supplier relations to optimize the entire supply chain process.

Primarily relies on A2A for complex inter-agent workflows

Research Assistant

Researchers use AI assistants that can access academic papers, experimental data, and simulation results to help analyze and synthesize complex scientific information.

Heavily uses MCP for accessing diverse data sources

Get Started with Your Use Case

The possibilities with these protocols are vast and growing. Whether you're building AI-powered tools or looking to enhance existing applications, A2A and MCP provide standardized ways to connect agents and data sources.

A2A GitHub Repository MCP GitHub Repository

Join the AI Agent Protocol Community

Be part of the growing ecosystem of developers, organizations, and enthusiasts building the future of AI agent communication and data access.

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Why These Protocols Matter