A2A vs MCP: Agent Chatter vs Contract Clarity
🧠 Introduction
So… your agents can talk, but can they do anything?
Welcome to the wild west of AI architecture, where LLMs aren't just generating text anymore — they're thinking, planning, and even calling each other. That’s where A2A (Agent-to-Agent) and MCP (Modular Contract Protocol) enter the scene. One handles the chatter. The other gets stuff done.
In this post, we’ll break down what A2A and MCP really mean, why they’re not the same, and how they fit together in your AI agent stack.
🤖 What is A2A (Agent-to-Agent)?
Imagine a Slack channel full of highly skilled coworkers. One says, "Hey, I’ll research this." Another replies, "Cool, I’ll summarize it." This is A2A: agents communicating with other agents.
In technical terms, A2A is:
- A communication pattern
- Between autonomous or semi-autonomous agents
- That collaborate, delegate, or negotiate tasks
🔧 Tech Examples
- LangGraph: Graph-based agent flows
- AutoGen: Multi-agent loops
- CrewAI: Role-based agents with memory
✅ When to Use A2A
- When tasks require reasoning and delegation
- For goal-driven planning
- In multi-agent simulations or research environments
❌ Challenges
- Harder to debug
- Emergent behaviors (unexpected outcomes)
- Requires robust context passing or memory
🛠️ What is MCP (Modular Contract Protocol)?
Now imagine those Slack agents grabbing tools off a shelf — tools that are clearly labeled, versioned, and tested. That’s what MCP brings to the table.
An MCP is:
- A schema-defined interface for tools or APIs
- Structured using Zod or similar validators
- Automatically documented via OpenAPI
- LLM-friendly: agents know what inputs to send and what outputs to expect
📈 Hacker News MCP Example (Live Demo / Blog)
You wrapped the Hacker News API in a contract-first way using:
- Zod for input/output validation
- zod-openapi for docs
- Clean folder structure and LLM-ready endpoints
Now any AI agent can call it confidently, without guesswork.
✅ When to Use MCP
- When building agent-compatible APIs
- For plugin systems, tool registries, or microservices
- To simplify observability and reduce bugs
❌ Challenges
- More rigid than free-form agent logic
- Needs upfront schema design
⚔️ A2A vs MCP: Key Differences
| Feature | A2A (Agent-to-Agent) | MCP (Modular Contract Protocol) |
|---|---|---|
| What it is | Communication between agents | Structured tool definition |
| Purpose | Delegation, reasoning, multi-agent logic | API/tool interface standard for agents |
| Structure | Decentralized, memory/context-driven | Schema-first, contract-based |
| Validation | Implicit (via reasoning) | Explicit (zod, OpenAPI) |
| Observability | Complex, often emergent | Transparent, contract logs |
| Examples | LangGraph, CrewAI, AutoGen | OpenAI functions, ai-agent-flow, LangChain Tools |
| Best For | Dynamic planning and collaboration | Reliable tool usage and plugins |
“A2A is like a group project where agents argue over who does what. MCP is the well-labeled toolbox they grab when it’s finally time to build.”
📊 Real-World Example
Let’s say you’re building a content generation agent.
- The Planner Agent creates a task list and delegates to:
- A Research Agent, which uses a web search tool (MCP)
- A Writing Agent, which calls a Markdown Formatter tool (MCP)
- A2A = Coordination and delegation
- MCP = Execution and structure
Together, they form a clean, traceable, scalable workflow.
🧠 Final Thoughts
A2A gives your agents brains and personalities. MCP gives them arms and legs.
If you're serious about building AI systems that can plan and execute, you'll want both: smart agents that can reason and a structured world of tools they can reliably call.
🔗 Learn More
❓ FAQs
Is A2A better than MCP? No — they serve different purposes. A2A is for agent logic. MCP is for tool interaction.
Can I use both together? Absolutely. That’s the sweet spot for most AI workflows.
How do I create an MCP-ready API? Use input/output validation (e.g., zod), generate OpenAPI docs, and follow clean folder structures.