Introduction

Modern AI systems are increasingly built as agents that can interact with tools, data sources, and other agents to accomplish complex tasks. Two important architectural standards have emerged to facilitate these interactions: the Model Context Protocol (MCP) and the Agent-to-Agent (A2A) protocol. This guide explores both architectures, highlighting their differences, suitable scenarios, and providing sample projects to get started

Key Points

• Agentic MCP (Model Context Protocol) enhances single AI agents by connecting them to tools and data, while A2A (Agent-to-Agent) enables multiple agents to communicate and collaborate.

• It seems likely that MCP and A2A are complementary, not competitive, with MCP focusing on tool access and A2A on inter-agent coordination.

• The evidence leans toward using MCP for tasks needing external resources and A2A for multi-agent workflows, with ongoing development shaping their future.

Agentic MCP and A2A Overview

Agentic MCP (Model Context Protocol) is a protocol that helps AI models, like chatbots, connect to external systems such as databases or business tools. It’s like giving the AI a set of tools to fetch information or perform tasks during a conversation, making it smarter and more helpful.

A2A (Agent-to-Agent) Architecture is about letting different AI agents talk to each other. Imagine a team where each agent has a job, like one handles customer queries and another manages tickets — they can work together smoothly using A2A.

Both are part of the growing field of AI agents, which are software entities that reason and act on behalf of users, and they’re shaping how we build intelligent systems.

4. Agentic MCP Architecture

4.1 What is MCP?

The Model Context Protocol (MCP) is an open standard introduced by Anthropic that standardizes how AI models connect with external tools, data sources, and services. It acts as a “USB-C port” for AI applications, providing a universal interface between models and the outside world.

4.2 Core Components

MCP follows a client-server architecture with the following key components:

1. MCP Hosts — Programs using LLMs (like Claude Desktop or IDEs) that initiate connections to access external data and tools

2. MCP Clients — Protocol clients embedded within the host application that maintain 1:1 connections with servers

3. MCP Servers — Lightweight programs that expose specific capabilities through the standardized protocol

4. Data Sources — Both local (files, databases) and remote services (APIs) that MCP servers can access

4.3 MCP Architecture Diagram

The diagram illustrates how an MCP client connects to multiple MCP servers, each providing different capabilities such as search functionality, custom prompts, email operations, and web search.

4.4 How MCP Works

MCP standardizes three main types of capabilities:

1. Tools — Functions that can be called by the LLM (with user approval)

2. Resources — File-like data that can be read by clients (API responses, file contents)

3. Prompts — Pre-written templates to help users accomplish specific tasks

The protocol operates through a defined connection lifecycle:

1. Client sends an initialize request with protocol version and capabilities

2. Server responds with its own protocol version and capabilities

3. Client sends an initialized notification as acknowledgment

4. Normal message exchange begins using request-response patterns and notifications

5. Either party can terminate the connection through various methods

MCP uses JSON-RPC 2.0 as its message format, transported via either standard I/O (for local processes) or HTTP with Server-Sent Events (for remote communication).

5. A2A Architecture

5.1 What is A2A?

The Agent-to-Agent (A2A) protocol, introduced by Google, is an open standard designed to enable AI agents to communicate, share information, and collaborate effectively, regardless of the underlying framework or vendor.

5.2 Core Components

A2A architecture centers around facilitating communication between agents with these key components:

1. Client Agent — Formulates tasks and communicates them to remote agents

2. Remote Agent — Acts on tasks to provide information or perform actions

3. Agent Card — JSON metadata file describing an agent’s capabilities and endpoints

4. Task Management — Defines task objects with lifecycle stages and outputs

5. Messaging System — Allows agents to exchange context, replies, and artifacts

5.3 A2A Architecture Diagram

5.4 How A2A Works

A2A facilitates agent-to-agent communication through:

1. Capability Discovery — Agents advertise their skills via Agent Cards in JSON format

2. Task Management — Communication oriented toward task completion with defined lifecycle stages

3. Collaboration — Agents exchange messages containing context, replies, or artifacts

4. User Experience Negotiation — Messages include parts with specified content types

The protocol is built on established web standards (HTTP, SSE, JSON-RPC) and is secure by default with enterprise-grade authentication.

6. MCP vs A2A: Key Differences

6.1 Focus and Purpose

• MCP focuses on connecting a single AI model to external tools and data sources. It’s primarily about extending what a single agent can do.

• A2A focuses on enabling collaboration between multiple agents. It’s about coordinating distributed agent ecosystems.

6.2 Interaction Mode

• MCP uses function calls and structured I/O for predictable, low-level tool access.

• A2A uses conversational interactions supporting long-running tasks and dynamic coordination.

6.3 Abstraction Level

• MCP operates at a lower level with explicit instructions for specific functionalities.

• A2A functions at a higher level focused on intent and capabilities between agents.

6.4 Comparison Table

7. Suitable Scenarios for MCP

MCP is better suited for scenarios where:

1. Direct Tool Integration — AI assistants need access to structured data (databases, files, APIs)

2. Runtime Tool Discovery — Development teams prefer runtime tool discovery over pre-programmed connections

3. Data Access with Existing Infrastructure — Teams want to share internal data while keeping their existing infrastructure

4. Specialized Tools — Applications require direct access to specialized tools and data sources

Real-world examples where MCP excels:

• Development environments where coding assistants need live coding context

• Data analysis applications requiring access to multiple databases and APIs

• Financial applications where AI needs secure access to internal financial data

8. Suitable Scenarios for A2A

A2A is more suitable for scenarios requiring:

1. Multi-Agent Collaboration — Complex workflows needing multiple specialized agents working together

2. Cross-System Automation — Enterprise workflows that cross department lines

3. Vendor-Agnostic Agent Interoperability — Getting agents from different vendors to work together

4. Complex Workflows — End-to-end processes like employee onboarding across HR, IT, and finance

Real-world examples where A2A excels:

• Hiring workflows with different agents for sourcing, screening, and scheduling

• Customer support systems spanning multiple backend services

• Complex research tasks distributed across specialized knowledge agents

9. Complementary Nature of MCP and A2A

Rather than competing alternatives, MCP and A2A are complementary technologies that can be used together:

• MCP provides a standard way for agents to access tools and data sources

• A2A provides a standard way for agents to collaborate and coordinate

In a well-designed system, agents can use MCP to connect with their tools and data, while using A2A to collaborate with other agents on complex tasks.

10. MCP Sample Project

To get started with MCP, you can implement a simple MCP server that exposes tools and resources:

10. 1 Project Setup:

# Install the uv package manager
curl -LsSf https://astral.sh/uv/install.sh | sh

# Create and initialize the project
uv init mcp-server
cd mcp-server

# Create virtual environment and activate it
uv venv
source .venv/bin/activate  # On Windows use: .venv\Scripts\activate

# Install dependencies
uv add "mcp[cli]" httpx

10.2 Create a basic MCP server (e.g., main.py):

from fastmcp import FastMCP

# Create an MCP server
mcp = FastMCP("Demo Server")

# Register a search function
@mcp.tool("search_docs")
async def search_docs(query: str) -> str:
    """Search for documentation related to the query."""
    # In a real implementation, this would search a database or API
    return f"Results for: {query}"

# Register a custom prompt
@mcp.prompt("code_review")
async def code_review() -> str:
    """Return a template for code review."""
    return "Review the following code for:\n- Bugs\n- Efficiency\n- Readability"

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

10.3 Run the server:

10.4 Connect with Claude Desktop:

• Install Claude Desktop

• Edit the configuration file to connect to your MCP server

11. A2A Sample Project

For A2A, you can implement a simple currency conversion agent based on Google’s example:

11.1 Setup the Environment:

11.2 Run the A2A Agent:

11.3 Test with Client:

11.4 Example Interaction:

You can test the agent with queries like “Convert 100 USD to EUR” and see how it handles the conversation and performs the currency conversion task.

12. Conclusion

As of April 14, 2025, MCP and A2A represent two dimensions of AI agent development — MCP for tool integration and A2A for agent collaboration — signaling a shift from deterministic programming to autonomous, collaborative systems. Their complementary nature suggests a future where AI applications can think, adapt, and collaborate, revolutionizing software development. Teneo.ai and other platforms are monitoring these protocols for enterprise integration, with ongoing discussions on challenges like security, scalability, and legacy system adaptation (MCP and A2A Protocols Explained The Future of Agentic AI … | Teneo.ai, A2A and MCP: Start of the AI Agent Protocol Wars? | Koyeb).