All You Need to Know About MCP, A2A, and Real-Life Use Cases

Introduction

In the rapidly evolving landscape of AI agent systems, two protocols—Model Context Protocol (MCP) and Agent-to-Agent Protocol (A2A)—are emerging as foundational technologies. While these protocols are relatively new, understanding their distinct roles and how they complement each other is essential for building scalable, flexible, and intelligent multi-agent systems.

This article aims to demystify MCP and A2A by explaining what each protocol is, why they are needed, and how they function in real-world applications. Through a detailed example use case—a smart building visitor management system—you will see how these protocols enable agents to access external tools and data (via MCP) and collaborate seamlessly as autonomous peers (via A2A). By following this example, you will gain a clear understanding of the differences between MCP and A2A, their complementary nature, and practical insights into how they can be integrated to create robust AI ecosystems.

What Are MCP and A2A?

MCP (Model Context Protocol)

MCP (Model Context Protocol) is a standardized protocol developed by Anthropic that enables AI systems to connect to MCP servers, which provide specific capabilities and external resources. It functions similarly to how USB-C standardizes connections for electronic devices. When an agent, such as a large language model (LLM), connects to an MCP server, it can utilize its capabilities to call functions, execute queries, or listen to events.

Example: An MCP server with facial recognition capability may expose:

{
  "capability": "faceRecognition.search",
  "input": {"image": "base64 string"},
  "output": {"name": "John Doe", "apartment": "5B"}
}        

A2A (Agent-to-Agent Protocol)

A2A is a protocol developed by Google that enables direct communication between agents using two primary communication models:

1. Synchronous Communication (Request-Response):

• Immediate response requirements
• Direct request-response pattern
• Suitable for quick tasks and simple queries
• Implemented through tasks/send method

2. Asynchronous Communication (Notifications):

• Ongoing updates and longer processing times
• Server-Sent Events (SSE) for streaming updates
• Suitable for complex tasks requiring progress tracking
• Implemented through tasks/sendSubscribe method

This dual communication model enables seamless collaboration between agents, even across different frameworks and vendors.

How Agents Find and Connect to Each Other

MCP Discovery & Connection

1. Server Discovery: MCP servers expose their capabilities through a standardized interface.

2. Local-First Security:

• Servers run locally by default unless explicitly permitted for remote use.
• Every tool or resource access requires explicit user approval.
• Zero-trust architecture ensures all requests undergo authentication and parameter sanitization.

3. Connection: Agents initialize connection:

{
  "jsonrpc": "2.0",
  "method": "initialize",
  "params": {
    "capabilities": ["faceRecognition", "residentSearch"]
  }
}        

A2A Discovery & Connection

1. Agent Discovery: Agents expose their capabilities through Agent Cards at /.well-known/agent.json:

{
  "name": "FaceRecognitionAgent",
  "description": "Provides facial recognition services",
  "version": "1.0",
  "capabilities": ["faceRecognition"],
  "endpoint": "https://agent1.benraz.com/a2a",
  "authentication": {
    "type": "bearer",
    "description": "Bearer token required for all requests"
  },
  "supportedModes": {
    "input": ["image/jpeg", "image/png"],
    "output": ["application/json"]
  }
}        

2. Discovery Methods:

• Open Discovery: Direct HTTP GET requests to well-known URLs.
• Curated Discovery: Enterprise registries for internal agent catalogs.
• Private Discovery: Custom APIs with authentication for secure environments.

3. Communication Protocol:

• Uses JSON-RPC 2.0 over HTTPS.
• Supports multi-turn conversations.
• Enables streaming updates via Server-Sent Events (SSE).
• Manages task lifecycles and state tracking.

4. Security Features:

• Authentication schemes (Bearer tokens, API keys, mTLS).
• Protected sensitive information.
• Secure agent-to-agent communication.
• Enterprise-grade access controls.

Use Case: Smart Building Visitor Management

Imagine a smart building entrance system designed to:

1. Automatically recognize residents.
2. Allow or deny visitor access based on resident approval.
3. Provide friendly voice interaction.

Key Agents:

• Agent A: Assistant agent triggered when someone is at the door. It orchestrates the flow.
• Agent 1: Face recognizer using MCP to identify residents by image.
• Agent 2: Name recognizer using MCP to identify residents by full name.
• Agent 3: Access handler using MCP to open doors and speak.
• Agent 4: Resident communicator agent using MCP to call the resident for approval.

Note: In our system, each agent is powered by a large language model (LLM) and interacts with external tools and hardware via an intermediate MCP server. The MCP server handles integration with third-party APIs, SDKs, and devices, exposing their capabilities through a standardized, structured, and secure interface. MCP is essential for our agents because it abstracts the complexity of integrating these elements, allowing LLMs to focus on processing and decision-making. This abstraction enables LLM-based agents to execute real-world tasks—such as controlling devices, retrieving data, or initiating communication—without dealing with low-level integration complexity.

Agent Roles Summary

Agent A (Assistant):

MCP: Controls and monitors building cameras:

• Monitors door camera feed for approaching visitors.
• Detects motion near the entrance.
• Captures facial images when visitors approach.
• Interfaces with building’s central camera system.

A2A: Orchestrates the flow by communicating with other agents:

• Sends captured face images to Agent 1 for recognition.
• Coordinates with Agent 2 for name verification.
• Triggers Agent 3 for door and speaker control.
• Requests approval through Agent 4.

Agent 1 (Face Recognition):

• A2A: Receives face image from Agent A (request).
• MCP: Calls MCP Server to compare face and return info.
• A2A: Sends back identification result (response).

Agent 2 (Name Recognition):

• A2A: Receives name from Agent A.
• MCP: Queries for resident info by name.
• A2A: Responds with resident info.

Agent 3 (Door Controller):

A2A Communication:

• Receives instructions and visitor details from Agent A.
• Sends confirmation responses after completing actions.

MCP Integration:

• Controls physical door hardware through door.control capability.
• Manages speaker system through speaker.announce capability.
• Ensures synchronized hardware operations.

Agent 4 (Resident Communicator):

• A2A: Receives request for approval.
• MCP: Calls the resident.
• A2A: Responds with approval status.

Agent Roles and Communication Flow

Step-by-step Example:

Step 1: Visitor Arrives

Agent A is triggered by the door camera.

Step 2: Face Recognition

A2A Request (from Agent A to Agent 1):

{
  "type": "identifyByFace",
  "image": "base64-string",
  "from": "AgentA"
}        

A2A Response:

{
  "status": "not-recognized"
}        

Step 3: Ask for Resident Name

MCP Request (Agent A → MCP Server):

{
  "capability": "microphone.capture",
  "params": {
    "deviceId": "entrance_mic",
    "mode": "voice",
    "duration": "10s",
    "noiseReduction": true,
    "format": "audio/wav"
  }
}        

MCP Response:

{
  "status": "success",
  "audioData": "base64-encoded-audio"
}        

Note: Agent A requires MCP connection to control the door’s microphone hardware. The microphone control involves: - Managing hardware characteristics (frequency response, directionality) - Noise cancellation for improved recognition accuracy - Secure audio capture and transmission - Integration with voice processing capabilities

After processing the voice input, Agent A extracts:

{
  "residentName": "Joseph Benraz",
  "visitorName": "Adam Levi"
}        

Step 4: Name-Based Lookup

A2A Request (Agent A → Agent 2):

{
  "type": "identifyByName",
  "name": "Joseph Benraz",
  "from": "AgentA"
}        

MCP Request (Agent 2 → MCP Server):

{
  "capability": "residentSearch.byName",
  "params": {"name": "Joseph Benraz"}
}        

MCP Response:

{
  "name": "Joseph Benraz",
  "apartment": "7C",
  "phone": "+123456789"
}        

Step 5: Ask Resident for Approval

A2A Request (Agent A → Agent 4):

{
  "type": "requestApproval",
  "residentName": "Joseph Benraz",
  "visitorName": "Adam Levi",
  "residentPhone": "+123456789",
  "from": "AgentA"
}        

MCP Request (Agent 4 → MCP Server):

{
  "capability": "resident.callAndAsk",
  "params": {
    "residentName": "Joseph Benraz",
    "residentPhone": "+123456789",
    "message": "Do you allow Adam Levi to enter?"
  }
}        

MCP Response:

{
  "approvalStatus": "yes"
}        

A2A Response (Agent 4 → Agent A):

{
  "approval": "yes"
}        

Step 6: Open Door

A2A Request (Agent A → Agent 3):

{
  "type": "allowVisitor",
  "visitorName": "Adam Levi",
  "residentName": "Joseph Benraz",
  "approvalStatus": "yes"
}        

MCP Requests (Agent 3 → MCP Servers):

// Door control request
{
  "capability": "door.control",
  "params": {
    "action": "open",
    "doorId": "main_entrance"
  }
}

// Speaker control request
{
  "capability": "speaker.announce",
  "params": {
    "message": "Welcome, Adam Levi. Joseph Benraz approved your visit."
  }
}        

A2A Response (Agent 3 → Agent A):

{
  "status": "success",
  "actions": {
    "door": "opened",
    "announcement": "completed"
  }
}        

Security Considerations

Both protocols include built-in security features to ensure safe and reliable operation.

MCP implements a local-first security approach:

• Requires explicit user approval for all tool access, reducing unauthorized usage.
• Enforces a zero-trust architecture, ensuring every request is authenticated and authorized.
• Implements hardware-based encryption to protect sensitive data.

A2A uses secure agent discovery and authentication:

• Ensures only authorized agents can communicate with each other.
• Prevents unauthorized access to agent capabilities.
• All communications use encrypted channels to maintain data integrity and confidentiality.
• Comprehensive logging and auditing of all security events provide traceability and help detect potential breaches.

Key Takeaways

• MCP is designed for connecting agents to capability-rich servers. It focuses on enabling functionality by providing access to external tools and resources.
• A2A is designed for communication between agents. It focuses on coordination and collaboration between autonomous agents.
• The protocols are complementary: MCP handles functionality within agents, while A2A handles the flow of information and tasks between agents.
• A fully working system requires both MCP and A2A to create a dynamic, distributed, and AI-driven environment.

Thank you for reading! I hope this article has provided you with valuable insights into the Model Context Protocol (MCP) and the Agent-to-Agent Protocol (A2A), and how these foundational technologies work together to enhance the capabilities of AI agents. Understanding these protocols is crucial for anyone looking to build or integrate intelligent systems in today's rapidly evolving technological landscape.

By leveraging MCP and A2A, we can unlock the full potential of AI agents, enabling them to seamlessly integrate into real-world systems and collaborate effectively. If you found this information insightful, please feel free to share it with others who may benefit from learning about these important developments in AI technology. Together, we can foster a deeper understanding of how these protocols are shaping the future of AI.