MCP vs A2A: Understanding AI Agent Protocols
Today we're diving into two technologies that are fundamentally changing modern software architecture: MCP (Model Context Protocol) and A2A (Agent-to-Agent). These systems are already working under the hood in ChatGPT, Claude, and other popular AI tools, and they'll be everywhere in the future.
Let's break it down — what's what, where to apply each, and why you'll probably need both protocols.
MCP and A2A in Plain English
MCP (Model Context Protocol) — think of this as the brain of an AI agent. The protocol manages the model's internal state, context, and tasks. When you chat with ChatGPT or interact with Claude, MCP is working behind the scenes.
A2A (Agent-to-Agent) — this is the communication language between different AI agents. Imagine you have several smart assistants who need to coordinate their actions. A2A is how they negotiate and collaborate with each other.
MCP — The Brain of the Operation
How MCP Works in Practice
Let's say you ask an AI to fact-check a post. Here's what happens under the hood:
1. Query Routing
The AI understands what it needs to do and selects the appropriate model for the task. It's like deciding which tool to grab for a repair job.
2. Tool Selection
The model determines which "tools" it will need:
- Search engine for information verification
- Calculator for number crunching
- Maybe a Python interpreter for complex calculations
3. Server Handoff
Selected tools receive their tasks. The search engine hunts for facts, the calculator crunches numbers. Each tool runs on its own "server" — could be anything from a local process to a cloud service.
4. Result Validation
Before delivering the answer, the system checks the result for sanity. Nobody wants to get "1+1=potato" as a response.
A2A — Conversation Between Brains
A2A kicks in when multiple AI agents need to work together. Classic example: you're chatting with ChatGPT and ask it to draw a picture of a cat dreaming of world domination.
ChatGPT doesn't draw images itself — it hands the task over to DALL-E. That's where A2A comes in: one agent tells another what needs to be done.
Key A2A Components
Agent Card — Agent Passport
This is like a README for your AI agent. It shows others:
- How to connect to it
- What it can do
- Usage examples
Task System
Classic CRUD for tasks:
- Create — create a task
- Read — check status
- Update — modify the task
- Delete — remove completed task
Secure Data Transfer
Agents should communicate over encrypted channels, like browsers through HTTPS. No eavesdropping allowed on their conversations.
When to Use What
| Scenario | MCP | A2A | Explanation |
|---|---|---|---|
| Controlling a single agent | ✅ | ❌ | Internal logic, memory, tool selection |
| Safety validation | ✅ | ❌ | Validating responses before user output |
| Multi-agent coordination | ❌ | ✅ | Task handoffs between different AIs |
| Multimodal tasks | ❌ | ✅ | Text + images + video from different models |
| Audit and logging | ✅ | ❌ | Tracking actions within a single agent |
Where to Find Ready-Made MCP Servers
By the way, if you need ready-made MCP servers, check out Fastmcp.me. They've curated an excellent collection of useful servers for various tasks:
- Servers for working with popular service APIs
- Data processing tools
- Database connectors
- Web scraping utilities
- And many other helpful tools
This is a huge time-saver — no need to build everything from scratch. You can grab ready-made solutions and adapt them to your needs.
The Future is Symbiosis
MCP and A2A don't compete — they complement each other:
- MCP makes agents smart from within
- A2A allows smart agents to work as a team
Imagine a system where you have an analyst agent (managed via MCP) that analyzes data and passes results to a designer agent (via A2A), which then creates beautiful charts and reports.
In the coming years, we'll see ecosystems of dozens of specialized agents, each with its own MCP and all connected through A2A. It'll be like an orchestra where each musician plays their part, but together they create a symphony.
Get ready for a future where AI agents become as commonplace as microservices are today.