MCP vs A2A: AI Agent Protocols Explained

Two protocols keep getting compared as rivals when they standardize different edges of the same system. MCP is how an agent reaches its tools and data. A2A is how agents reach each other. The cleanest summary is the official one, from the A2A documentation itself: "A2A focuses on agents partnering on tasks, whereas MCP focuses on agents using capabilities."

What each protocol standardizes

MCP (Model Context Protocol) connects a model-driven application to capabilities: tools it can call, resources it can read, prompts it can reuse. A tool is a primitive — structured input, structured output, often stateless. MCP's explosive 2025 made it the de facto standard for this layer: thousands of public servers, adoption across every major agent product, ~97M monthly SDK downloads by year's end. (Practical side: our setup guide and the 2026 server shortlist.)

A2A (Agent2Agent) connects agents — things that reason, plan, hold state across a long interaction, and use many tools internally. Its primitives are built for delegation between peers that don't share internals:

• Agent Card — a JSON document an agent publishes describing its identity, skills, service endpoint, and auth requirements. Discovery, solved declaratively.
• Task — the unit of work, stateful with a lifecycle: submitted → working → completed / failed / canceled, with input-required and auth-required interrupt states for human-in-the-loop and credential handoffs.
• Message / Parts — conversation turns between client and agent, carrying text, files, or structured data.
• Artifact — the durable outputs of a task (documents, data, images).

Transport is pragmatic: JSON-RPC, gRPC, or plain HTTP+JSON, per the v1.0 spec.

The auto-shop test

The A2A docs' canonical example survives because it maps to real systems. A repair shop: the customer talks to the shop (A2A — negotiation, clarification, a long-running task with updates), the shop's mechanic talks to the parts supplier (A2A again — two independent parties collaborating), and the mechanic uses the diagnostic scanner (MCP — a capability with structured inputs and outputs, no opinions of its own).

Swap in software: your support agent (A2A client) delegates a refund to the billing team's agent (A2A server), which internally calls Stripe and Postgres through MCP servers. A2A between organizations and teams; MCP inside each agent. They nest — which is why "versus" is the wrong preposition.

Do you need A2A yet?

Honest answer for most builders in mid-2026: MCP yes, A2A not yet.

• If your "multiple agents" are subagents inside one application — one codebase, one operator — your framework's orchestration plus MCP is simpler and sufficient. Multi-agent orchestration patterns don't require a wire protocol between processes you already control.
• A2A starts paying when the agents are independently operated: different teams in a large org, different vendors, or a product that exposes an agent for other people's agents to call. There, Agent Cards (discovery), task lifecycles (long-running work with interrupts), and standardized auth are exactly the problems you'd otherwise hand-roll badly.
• The strategic signal is governance: A2A launched with AWS, Microsoft, Salesforce, SAP, and ServiceNow at the table and hit spec v1.0 in March 2026 — enterprise agent-to-agent interop is being standardized ahead of mass demand, which is how useful protocols usually arrive.

Governance: both grew up

Neither protocol is a single vendor's anymore, and that matters for betting a roadmap on them:

• A2A — created by Google (April 2025), donated to the Linux Foundation in June 2025 as the Agent2Agent project; spec v1.0 landed March 2026; official SDKs in Python, JS, Java, Go, .NET, and Rust.
• MCP — created by Anthropic (November 2024), donated to the Linux Foundation's new Agentic AI Foundation in December 2025, co-founded with Block and OpenAI (alongside goose and AGENTS.md), with maintainers unchanged.

The practical upshot: build your tool layer on MCP today without lock-in anxiety, design your agent boundaries so a future A2A surface is a wrapper rather than a rewrite — an agent with clean task semantics and well-built tools underneath is already 80% of an A2A server — and re-evaluate A2A the day an agent you don't operate needs to call one you do.