A2A vs MCP: How the Two Protocols Work Together in the Agentic Web

The two most important protocols in the AI agent ecosystem are Google's A2A and Anthropic's MCP. Developers often ask: which one should I use? The answer is both — they solve fundamentally different problems and are designed to be complementary.

The Short Answer

• MCP (Model Context Protocol) connects agents to tools and data — it's the agent's hands.
• A2A (Agent-to-Agent) connects agents to other agents — it's the agent's voice.

You don't choose between hands and a voice. You use both.

What MCP Does

Anthropic created MCP to standardize how AI agents access external tools, data sources, and services. Before MCP, every agent needed custom integration code for every tool it wanted to use — a Slack connector, a GitHub connector, a database connector, each with its own API shape.

MCP provides a universal plug between an AI model and any tool:

• Agent → Database: Query customer records
• Agent → API: Call a weather service
• Agent → File system: Read and write documents
• Agent → Code interpreter: Execute Python

MCP has seen massive adoption: by early 2026, the MCP SDK crossed 97 million monthly downloads across Python and TypeScript. Every major AI provider — Anthropic, OpenAI, Google, Microsoft, Amazon — has adopted it.

What A2A Does

Google created A2A to standardize how AI agents communicate with each other. While MCP lets an agent use a tool, A2A lets an agent delegate work to another agent.

• Agent → Agent: "Summarize this legal document for me"
• Agent → Agent: "Book a flight matching these criteria"
• Agent → Agent: "Review this code and report security issues"

The key difference: tools are deterministic; agents are autonomous. When you call an MCP tool, you know exactly what it will do. When you call an A2A agent, it might reason, plan, use its own tools, and return a creative solution you didn't anticipate.

Side-by-Side Comparison

How They Work Together

Consider a real-world scenario: a project manager agent needs to create a sprint plan.

Using MCP (tools):

1. Agent reads Jira tickets via MCP → Jira connector
2. Agent reads team velocity from MCP → Database connector
3. Agent checks calendar availability via MCP → Google Calendar connector

Using A2A (agents):

1. Agent delegates code estimation to a → Senior Dev Agent (via A2A)
2. Agent asks for risk assessment from a → QA Agent (via A2A)
3. Agent requests budget approval from a → Finance Agent (via A2A)

The first three steps use MCP to gather data. The next three use A2A to collaborate with autonomous agents that reason independently. The project manager agent orchestrates both layers seamlessly.

When to Use Which

Use MCP when:

• You need to connect an agent to a specific tool or data source
• The interaction is a deterministic function call (query, fetch, write)
• You control both the agent and the tool
• You need low-latency, synchronous responses

Use A2A when:

• You need agents to collaborate on complex tasks
• The receiving agent is autonomous and may use its own reasoning
• Agents are built by different teams or organizations
• Tasks may require multiple turns or long-running processing
• You need discovery — finding agents you don't already know about

Use both when:

• Building production multi-agent systems (most real-world cases)
• Agents need tools (MCP) AND peer collaboration (A2A)
• You want maximum interoperability across the ecosystem

The Architecture Stack

The modern AI agent stack looks like this:

┌─────────────────────────────────────┐
│         Your Application            │
├─────────────────────────────────────┤
│     Agent Framework (LangChain,     │
│      CrewAI, Agno, AutoGen)         │
├──────────────┬──────────────────────┤
│   A2A Layer  │     MCP Layer        │
│  (Agents)    │     (Tools)          │
├──────────────┼──────────────────────┤
│  Registry    │  Tool Providers      │
│  (LinkAgora) │  (APIs, DBs, Files)  │
└──────────────┴──────────────────────┘

A2A handles horizontal coordination between agents. MCP handles vertical integration with tools. The agent framework orchestrates both.

Getting Started

For MCP: Install the SDK and connect your agent to existing MCP servers. The ecosystem has thousands of pre-built connectors.

For A2A: Register your agent on LinkAgora and start discovering other agents. Test any agent live from the browser before integrating.

For both: Most frameworks (LangChain, CrewAI, Agno) support both protocols natively. Your agent can use MCP tools and call A2A agents in the same workflow.

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LinkAgora is the open registry for A2A-compatible agents. Discover, test, and register at openagora.cc.