A2A vs MCP: Understanding the Two Protocols Shaping Agent Communication

A2A vs MCP: Understanding the Two Protocols Shaping Agent Communication

Two protocols have emerged as the foundational building blocks of the modern agentic web: Google's Agent2Agent (A2A) protocol and Anthropic's Model Context Protocol (MCP). Both are open standards. Both use JSON-RPC under the hood. Both are critical to how AI systems will communicate in production. But they solve fundamentally different problems—and understanding the distinction matters if you're building anything serious with agents.

The short version: MCP gives an agent access to tools. A2A lets agents talk to other agents. But the nuance is worth unpacking.

What Is MCP?

Model Context Protocol was introduced by Anthropic in late 2024. Its inspiration came from the Language Server Protocol (LSP)—the standard that lets IDEs like VS Code work with any programming language through a consistent interface. MCP takes the same idea and applies it to AI: a single protocol for connecting LLMs to external data sources, APIs, and tools.

MCP operates along a client-server model:

• •Host: The LLM application (e.g., Claude Desktop, your agent runtime)
• •Client: A connector inside the host that manages connections
• •Server: A service exposing tools, resources, or prompts

The three primitives MCP servers can expose are:

• •Resources: Read-only data the model can reference (files, database rows, API responses)
• •Tools: Functions the model can call (send email, query a database, run code)
• •Prompts: Templated message workflows

Communication happens over JSON-RPC 2.0. A server advertises its capabilities, the host negotiates which to use, and from there the agent can call tools in a structured, permissioned way.

// Example MCP tool invocation request
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "search_crm",
    "arguments": {
      "company": "Acme Corp",
      "limit": 10
    }
  }
}

The key insight: MCP is about extending what a single agent can do. It doesn't model collaboration between agents—it models an agent reaching into the world.

What Is A2A?

The Agent2Agent protocol was announced by Google in April 2025, with backing from over 50 technology partners including Salesforce, SAP, Atlassian, MongoDB, and service firms like Accenture and Deloitte. It's now maintained as an open-source specification at a2a-protocol.org.

Where MCP extends a single agent, A2A is explicitly designed for agent-to-agent collaboration. It defines how a "client agent" delegates tasks to a "remote agent" and tracks that work through a full task lifecycle.

A2A is built on:

• •HTTP + JSON-RPC 2.0 for request/response
• •Server-Sent Events (SSE) for real-time streaming of long-running tasks
• •Agent Cards for capability discovery (served at /.well-known/agent-card.json)
• •Task objects with a formal lifecycle: submitted → working → input-required → completed/failed

A typical A2A interaction looks like this:

1. 1.A client agent fetches the remote agent's Agent Card to understand its capabilities
2. 2.The client sends a message/send or message/stream request with a task
3. 3.The remote agent processes it, potentially streaming updates back via SSE
4. 4.When complete, the remote agent returns an artifact (the output)
5. 5.The client assembles results from multiple remote agents into a final response

// Agent Card (served at /.well-known/agent-card.json)
{
  "name": "SupplierPricingAgent",
  "description": "Returns current pricing and availability for catalog items",
  "url": "https://api.acmecorp.com/a2a",
  "capabilities": {
    "streaming": true,
    "pushNotifications": false
  },
  "skills": [
    {
      "id": "get-pricing",
      "name": "Get Pricing",
      "description": "Returns price quotes for given SKUs and quantities",
      "inputModes": ["text", "application/json"],
      "outputModes": ["application/json"]
    }
  ],
  "authentication": {
    "schemes": ["Bearer"]
  }
}

Side-by-Side Comparison

They're Complementary, Not Competing

This is the key point that's often missed: A2A and MCP are designed to work together.

In a real multi-agent system, you'll typically see both:

• •Each individual agent uses MCP to access its tools—CRM APIs, code execution, document retrieval
• •The orchestrator uses A2A to delegate tasks to specialized agents

Google explicitly acknowledged this in the A2A announcement: "A2A is an open protocol that complements Anthropic's Model Context Protocol (MCP), which provides helpful tools and context to agents."

Consider a hiring workflow:

Orchestrator Agent (A2A client)
    ├── CandidateSourcerAgent (A2A remote) → uses MCP to query LinkedIn, GitHub
    ├── SchedulingAgent (A2A remote) → uses MCP to access Google Calendar
    └── BackgroundCheckAgent (A2A remote) → uses MCP to call screening APIs

Each remote agent is an MCP-powered specialist. The orchestrator speaks A2A to coordinate them. This layered architecture is where things get powerful.

When to Use Which

Use MCP when:

• •You're building an agent that needs structured access to tools or data
• •You want to connect an existing LLM application to external APIs
• •You need a reusable, vendor-neutral way to expose your service as a tool

Use A2A when:

• •You need multiple specialized agents to collaborate on a task
• •Tasks are long-running and need state management across multiple turns
• •You want your agent to be discoverable by other agents in an ecosystem
• •You're building a platform where agents from different vendors interoperate

Use both when:

• •You're building production-grade multi-agent systems (which is most serious use cases)

The Bigger Picture

The emergence of both protocols signals a maturing of the agent ecosystem. We're moving from "AI as a feature" (a chatbot on a website) to "AI as infrastructure" (a fleet of agents executing business processes).

MCP solves the last-mile problem for individual agents: giving them structured, safe access to the real world. A2A solves the coordination problem: letting specialized agents work together without sharing code, memory, or vendor.

For platforms like Clawshake—where AI agents represent companies and negotiate deals on their behalf—both protocols are in play. An agent needs MCP to access company data and CRM systems, and A2A to communicate with counterpart agents from other companies in a structured, trustable way.

The agentic web is being built on these two protocols. Understanding both is now a prerequisite for serious agent development.