MCP vs. A2A: The Two Protocols Powering the Agentic Web

Model Context Protocol (MCP) and Agent-to-Agent Protocol (A2A) are not competing protocols - they solve different layers of the same problem. MCP standardizes how an AI agent connects to tools and data sources, while A2A standardizes how agents communicate and delegate tasks to each other. Together they form the foundational plumbing of the emerging agentic web.

If you’re building anything beyond a single-agent chatbot in 2026, you need to understand both.

The Fragmentation Problem

Before these protocols existed, the AI tooling space was a mess of incompatible integrations. Every major framework - LangChain , CrewAI , AutoGen - had its own proprietary way of connecting to external tools. Giving a LangChain agent access to the Slack API meant writing a LangChain-specific tool wrapper. Wanting the same capability in a CrewAI workflow meant starting over. Every framework required its own adapters, and none of them transferred.

The result was an integration tax that had nothing to do with actually building useful agents. Teams burned time on plumbing. Useful capabilities got locked behind framework-specific wrappers that couldn’t be shared.

Computing faced a similar problem before TCP/IP. Individual machines were useful in isolation but connecting them required bespoke point-to-point solutions for every pair. TCP/IP didn’t make any single computer better - it made all of them more useful by establishing shared terms for communication.

The agentic AI space needed the same kind of standardization. But the problem splits into two distinct layers:

1. Agent-to-tool connections - how does an agent access external capabilities like APIs, databases, and file systems?
2. Agent-to-agent communication - how do autonomous agents find each other, negotiate tasks, and exchange results?

MCP addresses the first. A2A addresses the second.

Model Context Protocol (MCP): The USB-C Port for AI

Anthropic released MCP in late 2024, originally as the mechanism for giving Claude reliable access to external data sources. The design was deliberately general: rather than building Claude-specific integrations, Anthropic defined a protocol that any model and any tool provider could implement.

The architecture has three components. The host is the AI application the user interacts with - Claude Desktop, the Gemini CLI, VS Code with an AI extension, or a custom agent. The client is the MCP connector embedded inside the host, handling protocol communication. The server is a lightweight process that exposes specific tools or data sources - one might wrap Slack, another your local file system, another a Postgres database.

An MCP server written once works with any compliant host. Build an MCP server for your company’s internal knowledge base and it becomes accessible to Claude Desktop, Gemini CLI, and any other MCP-compatible agent without additional integration work. No custom adapter per model, no framework-specific wrapper.

Here’s a minimal Python MCP server that exposes a single tool:

from mcp.server import Server
from mcp.server.stdio import stdio_server
from mcp import types

server = Server("my-tool-server")

@server.list_tools()
async def list_tools() -> list[types.Tool]:
    return [
        types.Tool(
            name="get_weather",
            description="Get current weather for a city",
            inputSchema={
                "type": "object",
                "properties": {
                    "city": {"type": "string", "description": "City name"}
                },
                "required": ["city"]
            }
        )
    ]

@server.call_tool()
async def call_tool(name: str, arguments: dict) -> list[types.TextContent]:
    if name == "get_weather":
        city = arguments["city"]
        # Your actual weather API call here
        return [types.TextContent(type="text", text=f"Weather in {city}: 22C, partly cloudy")]
    raise ValueError(f"Unknown tool: {name}")

async def main():
    async with stdio_server() as streams:
        await server.run(*streams, server.create_initialization_options())

if __name__ == "__main__":
    import asyncio
    asyncio.run(main())

Drop this into any MCP-compatible host’s configuration and that host immediately gains weather lookup capability - no framework-specific adapter required.

The Anthropic GitHub organization hosts official SDKs for Python and TypeScript. Beyond the official SDKs, the community has built thousands of MCP servers covering GitHub, Postgres, Google Drive, Notion, local file access, browser automation, and more. The protocol has moved well past “Anthropic-internal standard” into something the broader ecosystem has genuinely adopted.

Agent-to-Agent Protocol (A2A): The Network Layer for Multi-Agent Systems

MCP handles tool access well. What it doesn’t address is the higher-level problem: how do autonomous agents find each other, negotiate responsibilities, and coordinate on complex tasks across organizational or framework boundaries?

Google introduced A2A in April 2025. Unlike MCP - which remained under Anthropic’s stewardship - A2A was handed to the Linux Foundation for open governance almost immediately. Multi-agent infrastructure is too strategically important for broad adoption to sit under a single vendor’s control, and the Linux Foundation stewardship was a deliberate signal to the enterprise market.

A2A introduces three concepts that have no equivalent in MCP:

Agent Cards are JSON documents that function as a structured profile for an agent. They describe what the agent can do, how to reach it, what authentication it requires, and what kinds of tasks it accepts. When an orchestrator needs a specialist, it queries for Agent Cards and uses them to identify candidates.

Here’s a simplified Agent Card:

{
  "name": "ResearchAgent",
  "description": "Searches the web and summarizes findings on any topic",
  "version": "1.0.0",
  "url": "https://agents.example.com/research",
  "capabilities": {
    "streaming": true,
    "pushNotifications": false
  },
  "skills": [
    {
      "id": "web_research",
      "name": "Web Research",
      "description": "Searches and synthesizes information from the web",
      "inputModes": ["text"],
      "outputModes": ["text", "data"]
    }
  ],
  "authentication": {
    "schemes": ["bearer"]
  }
}

Dynamic discovery means an orchestrator doesn’t need to hardcode the address of a specific sub-agent. It searches for agents whose cards match the required capability profile. Multi-agent systems can adapt as new agents become available without manual reconfiguration.

Artifacts are the standardized output format for completed tasks. When a specialist finishes its work, it packages the result in an Artifact - a structured object with content, type metadata, and provenance. The orchestrator receives it via the same A2A channel it used to assign the task.

The cross-framework interoperability this enables is the real payoff. A LangChain orchestrator can delegate to a CrewAI specialist. A custom Python agent can hand off to an AutoGen workflow. As long as both sides implement A2A, the underlying framework is irrelevant. For a practical look at how multi-agent coordination patterns play out in real workflows, LangGraph’s stateful graph model is one of the cleaner implementations available today.

Head-to-Head: Key Differences

MCP is the USB port standard - it defines a physical interface so any device plugs into any computer. A2A is more like email - a protocol that lets independent entities address each other and coordinate without knowing anything about each other’s internals.

When to use each:

• MCP alone: a single agent connecting to local tools and data. The classic chatbot-plus-tools setup.
• A2A alone: multiple specialized agents coordinating with each other, each working from its own pre-configured tool set without needing MCP.
• Both together: autonomous workflows where orchestrator agents delegate via A2A to specialist agents, and those specialists use MCP to reach their required tools.

Better Together: The Unified Agentic Workflow

A concrete example shows why the combination matters. Take a “Project Manager Agent” tasked with researching a competitive landscape and drafting a summary document.

The workflow:

1. The Project Manager Agent receives the task. It queries the A2A network and finds a Researcher Agent whose card lists web research as a capability.
2. Via A2A, the Project Manager delegates the research subtask, specifying the topic and expected output format.
3. The Researcher Agent accepts the task and uses two MCP connections: a Search MCP server wrapping a web search API, and a Google Docs MCP server providing read access to a shared document library.
4. The Researcher gathers and synthesizes its findings, then packages the result as an A2A Artifact.
5. That Artifact flows back to the Project Manager, which uses it to generate the final deliverable.

MCP handles the tool access the specialist agent needs to do its work. A2A handles discovery, delegation, and result transfer between the agents. Pull out either layer and the workflow collapses. This is why “Manager-Specialist” architectures depend on both protocols rather than one.

Challenges, Security, and What Comes Next

Adoption at scale introduces security problems that neither protocol fully solves on its own. The central issue is permission propagation. When a human user grants an orchestrator agent access to their email, calendar, and file system, those permissions shouldn’t automatically transfer to every sub-agent the orchestrator delegates to. An untrusted specialist receiving a task via A2A shouldn’t inherit the orchestrator’s full credential set.

Both protocols address this partially. MCP servers can declare granular capability constraints, limiting what any given client can request. A2A supports standard authentication schemes at the agent level, so each agent enforces its own access controls independently of the calling agent’s permissions. But composing these mechanisms correctly across a real multi-agent system requires careful architecture, and the tooling to make that straightforward is still maturing.

On adoption trajectory: MCP launched in late 2024 and by 2025 had become the default for tool access in the major AI development environments . A2A is newer, and the ecosystem around it is still building. The Linux Foundation stewardship matters here - enterprises are more willing to build critical infrastructure on protocols not controlled by a direct competitor.

A few things worth tracking over the next year:

The SDK gap between MCP and A2A is closing. MCP’s Python and TypeScript SDKs have been solid for a while. A2A tooling has been catching up quickly, and the quality of the developer experience tends to drive adoption more than the protocol spec itself.

Agent registries are starting to emerge. For A2A’s dynamic discovery to work in practice, you need infrastructure for publishing and searching Agent Cards. Both public registries (for widely available agents) and private enterprise registries (for internal specialist agents) are being built out.

Security tooling is the open problem. Production agentic deployments are accumulating faster than the guidance on how to compose MCP and A2A without creating privilege escalation risks . Expect frameworks and prescriptive recommendations to follow.

The protocols themselves are well-designed. The work now is building the surrounding infrastructure that makes them accessible to developers who aren’t already specialists in agentic system architecture.

Further reading and official resources:

• Model Context Protocol - Official Site
• MCP on GitHub (Anthropic)
• A2A Protocol - Official Site
• A2A on GitHub