The 30-Second Summary

MCP (Model Context Protocol) — created by Anthropic, released November 2024 — defines how an AI agent connects to tools and data sources. Think of it as USB-C for AI: a single standard plug that works with any tool server (databases, APIs, filesystems, browsers).

A2A (Agent-to-Agent Protocol) — created by Google, released April 2025 — defines how AI agents talk to each other. It standardizes the messages that flow between a client agent (the orchestrator) and a remote agent (the specialist), so you can build multi-agent systems across different vendors and frameworks.

MCP = agent ↔ tool. A2A = agent ↔ agent. They are complementary, not competing.

MCP in Depth

Before MCP, every AI framework had its own way of calling external tools. LangChain had its tool interface, AutoGen had function calling, CrewAI had its own abstraction. Each integration was custom-built and non-portable.

MCP standardizes this with a client-server architecture:

• MCP Client — lives inside the AI host (Claude Desktop, an IDE, a custom agent). Makes requests.
• MCP Server — wraps a tool or data source (a database, Slack, GitHub, a local filesystem). Responds to requests.
• Transport Layer — stdio (for local tools) or HTTP+SSE (for remote tools).

The protocol defines three primitives:

1. Tools — Functions the agent can call (e.g., search_web, read_file, query_db).
2. Resources — Data the agent can read (e.g., a document, a database row, an image).
3. Prompts — Templated instruction sets the server can provide to guide agent behavior.

The killer feature: write an MCP server once, and any MCP-compatible client can use it. The ecosystem now has 2,000+ MCP servers covering everything from web search to payment processing to home automation.

A2A in Depth

As multi-agent systems became production-grade, a new problem emerged: how does one agent delegate a task to another agent built on a different framework or hosted by a different vendor?

Google's answer is A2A. The protocol works around a concept called an Agent Card — a JSON-LD document hosted at /.well-known/agent.json that describes:

• What the agent can do (its skills and capabilities)
• How to reach it (endpoint URL)
• What authentication it requires
• What input/output formats it accepts

An orchestrator agent discovers a specialist's Agent Card, then communicates via standard HTTP tasks. A task has a lifecycle: submitted → working → completed / failed. Long-running tasks support streaming updates so the orchestrator stays informed.

A2A is deliberately opaque about internals — it doesn't care what framework or model powers the remote agent. This is intentional: it enables vendor-neutral multi-agent collaboration.

Side-by-Side Comparison

Dimension	MCP	A2A
Created by	Anthropic	Google (+ 50 partners)
Released	November 2024	April 2025
Core purpose	Agent ↔ Tool/Data	Agent ↔ Agent
Transport	stdio / HTTP+SSE	HTTP / JSON-RPC
Discovery	Config file / manual	Agent Card (/.well-known)
Ecosystem size	2,000+ servers	Growing rapidly
Best for	Tool integrations	Multi-agent orchestration
Framework support	Claude, LangChain, Cursor, etc.	LangGraph, CrewAI, ADK, etc.

Do They Compete or Complement?

Neither. A real-world production agent will use both:

• It uses MCP to connect to its tools — web search, code execution, database queries, file access.
• It uses A2A to delegate sub-tasks to specialist agents — a research agent, a writing agent, a code review agent.

Google's Agent Development Kit (ADK) actually demonstrates this combination explicitly: each sub-agent exposes an A2A interface for orchestration, while internally using MCP tools.

When to Use MCP (and When Not To)

Use MCP when:

• You need to connect an agent to external data or services (databases, APIs, local files).
• You want to build a reusable tool server that multiple agents can share.
• You're building within an ecosystem that already supports MCP (Claude, Cursor, Windsurf, many others).

Skip MCP when:

• You only need simple function calling within a single framework — the overhead isn't worth it.
• Your tool is tightly coupled to one agent and will never be reused.

When to Use A2A (and When Not To)

Use A2A when:

• You're building a multi-agent system where agents are developed or hosted independently.
• You need cross-vendor agent collaboration (e.g., a LangGraph orchestrator delegating to an ADK specialist).
• You want standard task lifecycle management with streaming updates.

Skip A2A when:

• Your multi-agent system is self-contained within one framework (CrewAI, AutoGen, etc.) — use the native API.
• You need synchronous, low-latency agent calls — A2A's HTTP overhead may not suit you.

Getting Started

For MCP, the fastest path is to use an existing server from the MCP server registry, or build your own using the official MCP SDK (Python or TypeScript).

For A2A, Google's A2A specification has a reference implementation, and the Agent Development Kit (ADK) has first-class A2A support out of the box.

Both protocols are indexed in the AgDex directory under the Core Frameworks category.

The Bottom Line

If you're building agents in 2026 and ignoring both of these protocols, you're building on a non-standard foundation that will require painful refactoring as the ecosystem matures. MCP is already essentially required for serious tool integration. A2A is the protocol most likely to define how enterprise multi-agent systems are wired together.

Learn both. Use MCP now. Evaluate A2A as your system grows into multi-agent territory.