Model Context Protocol (MCP) vs Agent to Agent (A2A) Protocol

Genai

Model Context Protocol (MCP) vs Agent to Agent (A2A) Protocol

What is MCP (Model Context Protocol)?

MCP is a protocol or architectural pattern that allows one model (usually an LLM) to operate in multiple isolated contexts — for different users, tasks, or agents — while preserving memory, goals, and interaction history per context.

✅ Think of MCP as one brain with many memory slots

MCP Architetcure

What is A2A (Agent-to-Agent) Communication?

A2A is a multi-agent architectural pattern where multiple agents, each with their own roles, goals, tools, and models, communicate with each other via messages or APIs to complete complex tasks collaboratively.

✅ Think of A2A as a team of AI workers, each with their own specialty, talking to each other.

Generic Architecture: A2A (Agent-to-Agent)

Comparison Table: MCP vs Agent-to-Agent (A2A)

Feature	MCP (Model Context Protocol)	Agent-to-Agent (A2A)
🔧 Architecture Type	Single agent/memory core with multi-context support	Multiple agents, each with isolated memory and personality
🧠 Core model	Usually one LLM used for all contexts	Each agent can use its own LLM/tools/config
🧳 Context Isolation	Yes – managed by protocol (context IDs)	Yes – each agent owns its own context
🔄 Communication Style	Internal — context switching	External — message passing or API calls
🎯 Goal Management	All goals managed within the same model	Each agent has its own goal and planner
🧱 Scalability	Limited by single model’s capacity	High – add more agents as needed
🧩 Flexibility	Lower — fixed model behavior, shared tools	Higher — agents can evolve, specialize
🔁 Memory Management	Shared memory or isolated by context ID	Independent long-term memory per agent
🧠 Use Case	Multi-user assistant, internal copilot, shared chatbot	Agent teams, autonomous workflow, AutoGen, CrewAI, CAMEL
⚡ Best For	Simpler, centralized systems	Complex, distributed, collaborative systems

When to Use Which?

If you need...	Use
A lightweight AI agent for multiple users/tasks	MCP
Agents that collaborate or specialize	A2A
Easier cost & infrastructure control	MCP
Modular, evolving AI system	A2A
Language model acting as multiple sub-agents	A2A or Hybrid