AI Agents

MCP vs A2A: Picking the Right Agent Communication Protocol

April 7, 20269 min readintermediateHélain Zimmermann

MCPA2AAgent ProtocolInteroperability

The AI agent ecosystem in 2026 has two dominant communication standards: Anthropic's Model Context Protocol (MCP) and Google's Agent-to-Agent (A2A) protocol. If you are building agent systems right now, you have probably encountered both, and you have probably been confused about which one to use. The short answer is that they solve different problems and complement each other well. The longer answer requires understanding what each protocol actually does, where they overlap, and where they diverge.

Both protocols now live under the Linux Foundation's Agentic AI Foundation, which signals that the industry is moving toward interoperability rather than fragmentation. But the practical question remains: when do you reach for MCP, when do you reach for A2A, and when do you need both?

What MCP Actually Does

Model Context Protocol standardizes how an agent (or LLM) connects to tools, data sources, and services. Think of it as the USB-C of the agent world: a universal plug between a model and the things it needs to interact with. I covered the protocol's architecture in detail in a previous article on MCP, but the key point here is scope.

MCP defines three core primitives:

Tools: operations the model can invoke (search a database, call an API, run a computation)
Resources: data the model can read (files, database records, configuration)
Prompts: reusable prompt templates that servers can expose

The protocol uses JSON-RPC 2.0 over stdio or HTTP with Server-Sent Events. A client (your agent or IDE) connects to one or more MCP servers, discovers what capabilities they expose, and calls them as needed.

Here is what a minimal MCP server looks like in Python:

from mcp.server import Server
from mcp.types import Tool, TextContent
import json

server = Server("weather-service")

@server.tool("get_weather")
async def get_weather(city: str) -> list[TextContent]:
    """Retrieve current weather for a given city."""
    # In production, call a real weather API
    weather_data = {
        "city": city,
        "temperature": 22,
        "condition": "partly cloudy",
        "humidity": 65
    }
    return [TextContent(
        type="text",
        text=json.dumps(weather_data)
    )]

@server.tool("get_forecast")
async def get_forecast(city: str, days: int = 5) -> list[TextContent]:
    """Retrieve weather forecast for the next N days."""
    forecast = [
        {"day": i + 1, "high": 20 + i, "low": 12 + i, "condition": "sunny"}
        for i in range(days)
    ]
    return [TextContent(
        type="text",
        text=json.dumps(forecast)
    )]

if __name__ == "__main__":
    server.run()

The MCP ecosystem has exploded: over 97 million installs across various MCP server packages as of early 2026, with integrations in Claude Desktop, VS Code, Cursor, and dozens of other clients. This adoption curve reflects genuine developer demand for a standard way to wire tools into LLM workflows.

What A2A Actually Does

Agent-to-Agent protocol solves a fundamentally different problem. Where MCP connects an agent to tools, A2A connects an agent to other agents. The distinction matters because agent-to-agent communication requires capabilities that tool-calling does not: task delegation, progress tracking, multi-turn negotiation, and capability discovery across organizational boundaries.

A2A introduces several key concepts:

Agent Card: a JSON document describing an agent's capabilities, skills, and authentication requirements (published at /.well-known/agent.json)
Task: the fundamental unit of work, with a lifecycle (submitted, working, input-required, completed, failed, canceled)
Message: communication between agents within a task, with structured parts (text, files, data)
Artifact: outputs generated by an agent during task execution

Here is an example A2A agent card:

{
  "name": "Research Agent",
  "description": "Performs deep research on technical topics",
  "url": "https://research-agent.example.com",
  "version": "1.0.0",
  "capabilities": {
    "streaming": true,
    "pushNotifications": true,
    "stateTransitionHistory": true
  },
  "skills": [
    {
      "id": "technical-research",
      "name": "Technical Research",
      "description": "Researches technical topics and produces summaries",
      "tags": ["research", "analysis", "summarization"],
      "examples": [
        "Research the latest advances in transformer architectures",
        "Compare GraphRAG approaches published in 2026"
      ]
    }
  ],
  "authentication": {
    "schemes": ["bearer"]
  },
  "defaultInputModes": ["text/plain", "application/json"],
  "defaultOutputModes": ["text/plain", "text/markdown"]
}

The agent card is the discovery mechanism. A client agent finds another agent's card, inspects its skills, and decides whether to delegate a task. This is similar to how GPT-5's dynamic tool search approaches capability discovery, but at the agent level rather than the tool level.

Architecture Comparison

The architectural differences between MCP and A2A reflect their different scopes.

MCP Architecture

MCP follows a client-server model. The LLM application (host) manages one or more MCP clients, each connected to an MCP server. Communication is synchronous request-response or streamed results. The host application maintains context and decides which tools to call.

Host Application (e.g., Claude Desktop)
├── MCP Client → MCP Server (Database tools)
├── MCP Client → MCP Server (File system tools)
└── MCP Client → MCP Server (API integrations)

The model sees a flat list of available tools from all connected servers and calls them as needed. There is no concept of delegating a complex task to a server; the server just exposes operations.

A2A Architecture

A2A is peer-to-peer (or client-agent to remote-agent). A client agent sends a task to a remote agent, and the remote agent works on it asynchronously. The remote agent may take minutes, hours, or even days to complete. Communication happens through messages exchanged within the task context.

Client Agent
├── discovers Remote Agent A (via Agent Card)
│   └── sends Task → receives Messages/Artifacts
├── discovers Remote Agent B (via Agent Card)
│   └── sends Task → receives Messages/Artifacts
└── aggregates results

The critical difference: in A2A, the remote agent has autonomy. It decides how to accomplish the task. It might use its own tools (potentially via MCP), call other agents (via A2A), or apply its own reasoning. The client agent does not micromanage.

Message Formats and Transport

MCP uses JSON-RPC 2.0, which is simple and well-understood. Requests have a method name and params; responses have a result or error. Transport options include stdio (for local servers) and HTTP with SSE (for remote servers).

A2A uses plain HTTP with JSON payloads. Tasks are created with POST requests, updated with messages, and can be monitored via SSE for streaming updates. The protocol also supports push notifications via webhooks for long-running tasks.

Both protocols support streaming, but they handle it differently. MCP streams tool execution results (useful for large data returns). A2A streams task progress updates and partial artifacts (useful for keeping a user informed about a multi-step workflow).

Authentication Models

MCP's authentication story has been, honestly, its weakest point. Early versions assumed local execution (stdio transport), where authentication was not a concern. The remote HTTP transport added basic auth support, but the protocol itself does not prescribe a specific auth mechanism. In practice, most MCP servers rely on API keys or OAuth tokens passed through environment variables or configuration.

A2A takes authentication more seriously from the start. Agent cards declare their authentication requirements (bearer tokens, OAuth, API keys), and the protocol defines how credentials are exchanged. This makes sense given that A2A is designed for cross-organizational communication where trust boundaries matter.

For multi-agent systems operating within a single organization, MCP's simpler auth model is usually sufficient. For agents communicating across organizational boundaries, A2A's explicit auth model is essential.

Ecosystem Maturity

As of April 2026, the ecosystem comparison is not even close in terms of raw adoption:

MCP:

97+ million package installs
Supported by Claude Desktop, VS Code, Cursor, Windsurf, and most major AI IDEs
Thousands of community-built servers for databases, APIs, file systems, cloud services
Strong tooling: official SDKs in Python, TypeScript, Java, Kotlin, C#

A2A:

Growing adoption, primarily among enterprise teams building multi-agent orchestration
Reference implementations from Google
Gaining traction in financial services, healthcare, and manufacturing where cross-system agent communication is critical
SDKs in Python and TypeScript

MCP's head start and simpler use case (tool integration) explain the adoption gap. Most developers need to connect their agent to tools before they need agent-to-agent communication. A2A adoption is accelerating as more teams move from single-agent prototypes to multi-agent production systems.

When to Use MCP

Use MCP when your primary need is connecting an agent to external capabilities:

Tool integration: your agent needs to query databases, call APIs, read files, or execute code
IDE and developer tool integration: you are building tools that should work across AI-powered editors
RAG system components: exposing retrieval, ranking, and reranking as callable tools
Single-agent architectures: one agent that needs access to many tools

MCP is the right choice when the intelligence lives in a single agent and you just need to give it arms and legs. The protocol's simplicity is a strength here; there is no overhead for task lifecycles or capability negotiation when you just need to call a function.

When to Use A2A

Use A2A when your primary need is inter-agent communication:

Multi-agent orchestration: an orchestrator delegates work to specialist agents
Cross-organizational agent communication: agents from different vendors or departments need to collaborate
Long-running workflows: tasks that take minutes to hours, requiring progress tracking and status updates
Capability discovery: you need to dynamically find agents that can handle specific types of work

A2A shines in scenarios where you have multiple agents with different LLM backends working together on complex tasks. The protocol's task lifecycle management and agent discovery mechanisms handle the coordination complexity that would otherwise require custom orchestration code.

When to Use Both

In most production multi-agent systems, you will use both protocols. This is by design, not a compromise.

Consider a financial analysis system:

A coordinator agent receives a user request to analyze a company
Via A2A, it discovers and delegates to a research agent, a financial modeling agent, and a compliance agent
Each specialist agent uses MCP to connect to its tools: the research agent uses MCP to query news APIs and document stores, the financial agent uses MCP to access market data and spreadsheet tools, the compliance agent uses MCP to check regulatory databases

# Simplified orchestration combining both protocols
import httpx
from mcp.client import ClientSession

class FinancialAnalysisOrchestrator:
    def __init__(self):
        self.a2a_client = httpx.AsyncClient()
        self.mcp_sessions: dict[str, ClientSession] = {}

    async def discover_agents(self):
        """Discover specialist agents via A2A agent cards."""
        agent_urls = [
            "https://research-agent.internal/.well-known/agent.json",
            "https://finance-agent.internal/.well-known/agent.json",
            "https://compliance-agent.internal/.well-known/agent.json",
        ]
        agents = {}
        for url in agent_urls:
            resp = await self.a2a_client.get(url)
            card = resp.json()
            agents[card["name"]] = card
        return agents

    async def delegate_research(self, company: str, agent_card: dict):
        """Delegate research task via A2A."""
        task_payload = {
            "jsonrpc": "2.0",
            "method": "tasks/send",
            "params": {
                "id": f"research-{company}",
                "message": {
                    "role": "user",
                    "parts": [{"type": "text", "text": f"Research {company}"}]
                }
            }
        }
        resp = await self.a2a_client.post(
            agent_card["url"],
            json=task_payload
        )
        return resp.json()

    async def get_market_data(self, ticker: str):
        """Use MCP to fetch market data directly."""
        session = self.mcp_sessions["market-data"]
        result = await session.call_tool(
            "get_stock_price",
            {"ticker": ticker}
        )
        return result

This layered approach, A2A for agent coordination, MCP for tool access, mirrors how human organizations work. Managers delegate tasks to specialists (A2A), and specialists use their tools to get work done (MCP).

The Convergence Under the Agentic AI Foundation

Both MCP and A2A are now governed by the Agentic AI Foundation under the Linux Foundation. This is significant because it means the protocols will evolve in a coordinated way rather than competing. The foundation includes OpenAI, Anthropic, Google, Microsoft, AWS, and Block as co-founders, which covers essentially all major players in the agent ecosystem.

What I expect to see over the next year:

Clearer handoff patterns between MCP and A2A, so the boundary between "tool" and "agent" becomes a smooth gradient
Unified authentication standards that work across both protocols
Agent discovery mechanisms that can find both MCP servers (for tools) and A2A agents (for delegation) through a common registry
Standardized telemetry for monitoring cross-protocol interactions

The fact that these protocols are complementary rather than competitive is the most important architectural insight. You are not choosing between MCP and A2A; you are choosing where in your system each one applies.

Practical Decision Framework

When evaluating which protocol to adopt, ask these questions:

Am I connecting an agent to tools, or connecting agents to each other? Tools → MCP. Agents → A2A.
Does the remote system have its own reasoning and autonomy? If yes, it is an agent (A2A). If it just executes operations, it is a tool (MCP).
Do I need task lifecycle management? Progress tracking, cancellation, multi-turn negotiation → A2A. Fire-and-forget function calls → MCP.
Am I crossing organizational boundaries? Different teams or companies → A2A (better auth, discovery). Same team → MCP is often sufficient.
How long does the operation take? Milliseconds to seconds → MCP. Minutes to hours → A2A.

For teams just starting with agents, begin with MCP. Get your tool integration solid. When you find yourself writing custom orchestration code to coordinate multiple agents, that is when A2A earns its place in your stack. And if you are building end-to-end multi-agent systems, plan for both from the start.

Key Takeaways

MCP standardizes how agents connect to tools and data; A2A standardizes how agents communicate with each other. They solve fundamentally different problems.
MCP uses a client-server model with JSON-RPC 2.0, while A2A uses a peer-to-peer task-based model with HTTP and JSON.
MCP has significantly higher adoption (97M+ installs) due to its simpler use case and earlier release, but A2A adoption is accelerating in enterprise multi-agent deployments.
In production multi-agent systems, you will typically use both: A2A for inter-agent coordination and MCP for each agent's tool access.
Both protocols now live under the Agentic AI Foundation, ensuring coordinated evolution rather than fragmentation.
Start with MCP for tool integration, then add A2A when you need multi-agent orchestration across trust boundaries.
The key architectural question is not "which protocol" but "where in my system does each protocol apply."
Authentication remains the area with the most room for improvement; expect unified auth standards from the Agentic AI Foundation in the coming months.