A2A vs MCP: What's the Difference?

Two protocols are shaping how AI agents work in 2026: A2A (Agent2Agent) and MCP (Model Context Protocol). They solve different problems, they work at different layers, and you'll likely need both. But the confusion between them is real.

This is the definitive comparison. No jargon, no filler — just what you need to know to make good decisions.

The One-Sentence Difference

MCP connects an agent to tools and data (vertical). A2A connects agents to each other (horizontal).

That's it. Everything else follows from this core distinction.

Side-by-Side Comparison

| Feature | MCP (Model Context Protocol) | A2A (Agent2Agent Protocol) |
|---|---|---|
| Created by | Anthropic (2024) | Google (April 2025), now Linux Foundation |
| Primary purpose | Connect agents to tools, APIs, and data sources | Enable agent-to-agent communication and collaboration |
| Direction | Vertical — agent ↔ tools/data | Horizontal — agent ↔ agent |
| Architecture | Client-server (agent = client, tool = server) | Client-server (requesting agent = client, remote agent = server) |
| Discovery | Tool manifests (what capabilities a server offers) | Agent Cards (JSON describing an agent's capabilities, auth, endpoint) |
| Transport | JSON-RPC over stdio or HTTP+SSE | JSON-RPC over HTTP+SSE |
| Auth model | Implementation-dependent | Enterprise-grade, OpenAPI-compatible auth schemes |
| Task model | Request-response (synchronous tool calls) | Tasks with real-time status updates, supports long-running work |
| Modalities | Primarily structured data and text | Text, files, audio, video streaming |
| Governance | Managed by Anthropic | Linux Foundation open-source project |
| Key partners | Anthropic, widely adopted across AI tools | Google, Salesforce, SAP, IBM, ServiceNow, 50+ partners |
| Best for | Giving an agent access to databases, APIs, file systems | Letting agents from different frameworks/vendors collaborate |

How MCP Works

MCP is Anthropic's protocol for connecting AI models to external tools and data. Think of it as a universal adapter.

An MCP server exposes capabilities — read a database, search GitHub, access a filesystem. An AI agent connects as a client and calls those capabilities as needed. The agent stays in control; the tools execute what's asked.

Examples in our directory: Anthropic MCP, MCP Server GitHub, MCP Server Filesystem, MCP Server SQLite.

MCP has become the de facto standard for tool integration. Most major AI platforms support it or are adding support.

How A2A Works

A2A is Google's protocol for agent-to-agent communication. Think of it as a common language between agents.

Every A2A-compliant agent publishes an Agent Card — a JSON file describing what it does, what inputs it accepts, how to authenticate, and where to reach it. Other agents read these cards to discover who can help.

When agents need to collaborate, one creates a task and sends it to a remote agent via A2A. The remote agent processes it — providing real-time updates for long-running work — and returns results as artifacts.

A2A launched with support from 50+ enterprise partners and was donated to the Linux Foundation in June 2025. IBM's ACP (Agent Communication Protocol) merged into A2A, consolidating the two major agent-to-agent standards.

When to Use Each

Use MCP when:

• Your agent needs to read from a database
• Your agent needs to call APIs or access external services
• Your agent needs file system access
• You're connecting an AI model to specific tools and capabilities
• You need structured, synchronous tool calls

Use A2A when:

• Multiple agents need to coordinate on complex workflows
• Agents from different vendors or frameworks need to work together
• You need long-running task delegation between agents
• Agents need to discover each other's capabilities dynamically
• You're building cross-organizational agent systems

Use both when:

• You're building real enterprise agent systems (this is most cases)
• Each agent uses MCP for its own tool access, and A2A for inter-agent collaboration
• You want agents that are both capable (MCP) and collaborative (A2A)

A Practical Example

Imagine an e-commerce operations system:

1. A customer service agent uses MCP to access the customer database, order history, and ticketing system.
2. That agent discovers (via A2A Agent Cards) a logistics agent maintained by your shipping partner.
3. Using A2A, the customer service agent delegates a "where's my package?" request to the logistics agent.
4. The logistics agent uses its own MCP connections to query carrier APIs and tracking databases.
5. It returns the tracking status via A2A back to the customer service agent.
6. The customer service agent responds to the customer.

MCP handled all the tool access. A2A handled the agent-to-agent coordination. Neither protocol could do both jobs alone.

Which Tools Support Which?

The ecosystem is evolving fast. Here's the current landscape:

Strong MCP support: Claude, Cursor, LangChain, most AI coding and productivity tools. Check our MCP hub for the full list. Growing A2A support: Google ADK (native A2A integration), LangGraph, CrewAI, AutoGen. The A2A GitHub repo includes sample implementations for all major frameworks. Supporting both: Google ADK supports both MCP (for tools) and A2A (for agent communication). This dual-protocol approach is becoming the standard pattern.

Common Misconceptions

"A2A replaces MCP." No. Google explicitly stated A2A complements MCP when they announced it. They solve different problems at different layers. "You have to choose one." No. In production systems, you'll use both. Every agent needs tool access (MCP) and most multi-agent systems need agent coordination (A2A). "MCP is just for Anthropic products." No. MCP is an open protocol adopted across the industry. It works with any AI model or framework. "A2A is just for Google products." No. A2A is now a Linux Foundation project with contributions from Google, IBM, Salesforce, SAP, and dozens of others.

What This Means for You

If you're building AI agent systems in 2026:

1. Learn both protocols. They're complementary, not competing. Understanding both puts you ahead of most builders.
2. Start with MCP if you're connecting a single agent to tools and data. It's more mature and widely supported.
3. Add A2A when you need multiple agents to work together, especially across vendor or organizational boundaries.
4. Pick frameworks that support both. Google ADK is the most integrated option today. LangGraph and CrewAI are adding support.
5. Watch the Linux Foundation A2A project. It's where the standard is evolving, and IBM's merger means enterprise adoption is accelerating.

For a deeper dive into A2A specifically, read our complete A2A guide. To explore MCP tooling, visit our MCP hub.