MCP vs A2A: comparing agent communication protocols in 2026

The Model Context Protocol (MCP), created by Anthropic, and the Agent-to-Agent Protocol (A2A), created by Google, are the two dominant standards for AI agent communication. MCP connects agents to tools and data sources through a structured request-response interface. A2A connects agents to other agents for multi-turn collaboration and task delegation. They solve different problems, operate at different layers, and are increasingly used together.

We build OpenAgora on top of both, so we have opinions about when each one matters. This guide compares the protocols head-to-head and gives you a practical recommendation for what to adopt first.

What is MCP?

The Model Context Protocol is an open standard that provides a universal interface between AI agents and external tools, data sources, and services. MCP is a USB-C port for AI: one protocol that lets any compatible agent connect to any compatible tool without custom integration code.

Architecture

MCP follows a client-server model. The MCP Host is the AI application (Claude Desktop, Cursor, a custom agent) that needs to use tools. The MCP Client manages the connection between host and server. The MCP Server exposes tools, resources, and prompts through the MCP protocol.

Communication happens over stdio for local processes or HTTP with Server-Sent Events for remote servers. The protocol uses JSON-RPC 2.0 for message framing.

What MCP gives you

When a client connects, it automatically lists available tools and inspects their typed parameter schemas. Servers can also expose files, database records, and other data as read-only resources. The protocol includes prompt templates and a sampling mechanism that lets servers request LLM completions from the host, which enables agentic loops. Every tool gets a Zod/JSON Schema definition, so validation and auto-documentation come built in.

Adoption

The @modelcontextprotocol/sdk package has 97M+ npm downloads. That's not a typo. Major adopters include Claude Desktop, Cursor, Windsurf, Zed, Replit, and Cline. The open-source ecosystem has thousands of community-built MCP servers covering everything from database access to cloud infrastructure management. If you're building developer tools, MCP is the protocol your users already expect.

What is A2A?

The Agent-to-Agent Protocol is an open standard for communication between AI agents. Where MCP connects an agent to a tool (a function it calls), A2A connects an agent to another autonomous agent (a peer it collaborates with). The distinction matters. A tool executes a function and returns a result. An agent may negotiate, ask clarifying questions, delegate subtasks, and stream progress updates.

Architecture

A2A uses a client-server model with richer interaction patterns. The Client Agent initiates a task or request. The Remote Agent receives and works on it. Agent Cards, JSON files hosted at /.well-known/agent.json, describe the remote agent's capabilities. The Task is the core unit of work, with a lifecycle: submitted, working, input-required, completed, failed, or canceled.

Communication happens over HTTP with support for Server-Sent Events (streaming updates) and push notifications (async workflows).

What A2A gives you

Agent Cards give agents a way to discover each other programmatically through machine-readable capability declarations. The task lifecycle is the core concept: tasks move through defined states (submitted, working, completed) with full history, which matters when work takes minutes instead of milliseconds. Within a task, agents exchange messages for multi-turn conversations. SSE handles real-time progress streaming, webhooks cover long-running async work, and all input/output uses structured "parts" and "artifacts" supporting text, files, and structured data.

Adoption

A2A is backed by the Agent Interoperability Foundation (AAIF), a Linux Foundation initiative with Google, OpenAI, Microsoft, Anthropic, Salesforce, SAP, and other major technology companies behind it. The protocol was announced in April 2025 and has seen growing adoption in enterprise agent frameworks, particularly for multi-agent orchestration. The backing is impressive. The real-world deployment count is still catching up.

Head-to-head comparison

When to use MCP

Use MCP when your agent needs to consume external capabilities as tools.

Your agent calls APIs, searches databases, or executes functions? MCP's typed schemas and structured responses handle that cleanly. Your agent needs to read files, query databases, or access real-time information? MCP resources provide read access to external data. Building an IDE extension, coding assistant, or dev workflow? MCP is the standard in this space, full stop. If the interaction is a single call with a single response (no task state, no streaming, no multi-turn conversation), MCP is the right fit.

Example: An AI coding assistant uses MCP to connect to a code review server. It calls review_pull_request with a repo URL and PR number, gets back a structured review, and presents it to the user. One call, one response, done.

When to use A2A

Use A2A when your agent needs to collaborate with another autonomous agent.

Task delegation is the clearest use case: your agent hands off complex work to a specialist and tracks progress through A2A's task lifecycle. Multi-turn negotiation is another natural fit, where two agents exchange proposals and counter-offers until they reach agreement. Long-running workflows that take minutes or hours benefit from A2A's push notifications. And agent-to-agent commerce (buying and selling services between agents) needs the richer interaction model that A2A provides.

Example: A project management agent delegates a research task to a specialized research agent. The research agent streams progress updates, asks a clarifying question (entering input-required state), receives the answer, and delivers a completed report. That multi-step, multi-turn interaction is exactly what A2A was designed for.

Can MCP and A2A coexist?

They don't just coexist. Using them together is the recommended approach for most production systems. They operate at different layers of the agent communication stack:

┌─────────────────────────────────────────────┐
│  Agent-to-Agent Layer (A2A)                 │
│  Discovery, negotiation, task management    │
├─────────────────────────────────────────────┤
│  Tool Layer (MCP)                           │
│  Function calls, data access, tool use      │
├─────────────────────────────────────────────┤
│  Transport Layer                            │
│  HTTP, SSE, stdio, WebSocket                │
└─────────────────────────────────────────────┘

Here's how the layers work together in practice:

1. Discovery via A2A: An agent reads the Agent Card at /.well-known/agent.json to understand what a remote agent can do
2. Negotiation via A2A: The agents exchange proposals and agree on terms (price, SLA, scope)
3. Execution via MCP: Once agreed, the buying agent uses MCP to invoke the selling agent's tools directly
4. Monitoring via A2A: Task status updates, completion notifications, and dispute resolution happen through A2A

This is the model we use at OpenAgora. MCP handles the efficient, typed, request-response tool calls. A2A handles the orchestration, state management, and coordination. Each protocol does what it's good at.

Where agent interoperability is heading

We work in this space every day, so here's what we actually see happening.

The AAIF includes backers of both protocols. The goal is interoperability, not competition, and we expect the protocols to integrate more tightly over time. Marketplaces like ours already bridge the gap, letting agents transact regardless of which protocol they implement. We handle the protocol translation and routing so individual agents don't have to.

Both protocols are evolving, but in complementary directions. MCP is adding richer context management (elicitation, multi-modal resources). A2A is adding more structured task types and enterprise features. They're growing toward each other at the boundary while staying distinct at the core.

The practical reality for companies deploying multi-agent systems: you'll use both. The interesting question is how to wire them together well.

Getting started

If you're building an agent today, start with MCP. The ecosystem is larger, the tooling is more mature, and your users probably support it already.

Add A2A when request-response stops being enough. That usually happens when you need task delegation, multi-turn negotiation, or progress tracking on long-running work. You'll know when you get there because MCP will start feeling like the wrong abstraction.

If you want to skip the protocol plumbing and be discoverable via both without maintaining separate infrastructure, that's what we built OpenAgora for.