A2A vs MCP: A Technical Comparison of the Two Agent Protocols That Matter

Two protocols now define how AI agents connect to the world. They solve different problems and are often confused for competitors.

Model Context Protocol (MCP), released by Anthropic in November 2024, defines how an LLM connects to tools and data sources: the vertical connection from model to capability. Agent2Agent (A2A), released by Google in early 2025 with over 50 enterprise partners, defines how AI agents communicate with each other: the horizontal connection from agent to agent.

The distinction sounds simple. In practice, the architectural implications are significant, the protocol designs differ substantially, and choosing the wrong one (or misunderstanding that you need both) leads to systems that do not compose well.

The sections below cover what each protocol does at the message level, how their trust and transport models work, where they overlap (less than most people think), and how to use them together in a real multi-agent architecture. Message format examples for both protocols show the concrete difference, not the marketing difference.

The OneLine Summary of Each Protocol

MCP: A standard for an LLM to call external tools and read data sources.

A2A: A standard for one AI agent to delegate tasks to another AI agent.

These are not substitutes. In a multi-agent system, you need both. MCP connects your agents to capabilities (APIs, databases, file systems). A2A connects your agents to each other. Framing them as competitors misses the design decision of where each protocol fits in your architecture.

MCP: How It Works

MCP (Model Context Protocol) uses JSON-RPC 2.0 as its wire format. A client (Claude Desktop, a custom application, an agent) connects to a server that exposes capabilities as tools, resources, or prompts.

The lifecycle is straightforward:

1. Client connects to server (via stdio or HTTP/SSE)
2. Client calls initialize: server responds with its capabilities
3. Client calls tools/list or resources/list: server returns available primitives
4. Client calls tools/call or resources/read with arguments: server executes and returns result

Every interaction is synchronous from the client's perspective. You send a request, you get a response. There is no concept of a task with a lifecycle. Either the call succeeds or it fails, immediately.

MCP Tools are the primary primitive. A tool has a name, a description, and a JSON Schema input spec. When called, it returns a list of content items (text, image, binary). The tool is a typed remote function call.

// MCP: tools/call request
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "search_web",
    "arguments": {
      "query": "LLM inference optimization 2025",
      "max_results": 5
    }
  }
}
 
// MCP: tools/call response
{
  "jsonrpc": "2.0",
  "id": 1,
  "result": {
    "content": [
      {
        "type": "text",
        "text": "**Result 1: Memory Bandwidth Is All You Need**\nhttps://example.com/bandwidth\nA guide to roofline model analysis for LLM inference..."
      }
    ],
    "isError": false
  }
}

MCP Resources are read-only data sources the LLM can access by URI. Unlike tools, resources are not called; they are read. A file, a database table, a live API endpoint can all be exposed as resources. Resources support subscriptions for live updates.

The MCP trust model is host-based. The server trusts the host (the application that spawned or connected to it). There is no per-request authentication in the core protocol; access control happens at the connection level.

A2A: How It Works

A2A (Agent2Agent) is an HTTP-based protocol for delegating tasks from one AI agent to another. Rather than exposing functions for an LLM to call, A2A exposes agents that can take on tasks and work toward goals asynchronously.

The central concepts:

Agent Card: A JSON document served at /.well-known/agent.json that describes what an agent can do. Unlike MCP's tool list (which requires a live connection to retrieve), Agent Cards are discoverable via standard HTTP. No integration required to learn what an agent offers.

// A2A: Agent Card (served at /.well-known/agent.json)
{
  "name": "SEO Research Agent",
  "description": "Researches keywords, analyzes SERPs, and generates content briefs",
  "url": "https://seo-agent.example.com",
  "version": "1.0.0",
  "capabilities": {
    "streaming": true,
    "pushNotifications": true,
    "stateTransitionHistory": true
  },
  "skills": [
    {
      "id": "keyword_research",
      "name": "Keyword Research",
      "description": "Analyzes keyword opportunities for a given topic and audience",
      "inputModes": ["text"],
      "outputModes": ["text", "data"]
    }
  ],
  "authentication": {
    "schemes": ["bearer"]
  }
}

Task: The core interaction primitive in A2A. A client agent sends a task to a remote agent. The task has a lifecycle: submitted → working → completed | failed | cancelled. The client can poll for status, stream progress updates via SSE, or receive push notifications on completion.

// A2A: Send task request (POST /tasks/send)
{
  "id": "task-abc-123",
  "message": {
    "role": "user",
    "parts": [
      {
        "type": "text",
        "text": "Research keyword opportunities for 'LLM inference optimization' for a technical B2B SaaS blog targeting ML engineers."
      }
    ]
  }
}
 
// A2A: Task status response (GET /tasks/{id})
{
  "id": "task-abc-123",
  "status": {
    "state": "completed",
    "timestamp": "2025-03-29T14:23:11Z"
  },
  "artifacts": [
    {
      "name": "keyword_report",
      "parts": [
        {
          "type": "text",
          "text": "## Keyword Research Report\n\n**Primary opportunity**: 'LLM inference optimization' (KD 28, 1,200 monthly searches)..."
        }
      ]
    }
  ]
}

The A2A trust model is explicitly credential-based. Each agent authenticates incoming requests using OAuth 2.0, API keys, or other standard HTTP auth schemes declared in the Agent Card. This is fundamentally different from MCP's host-based trust. A2A is designed for cross-organizational agent communication where you cannot assume trust by proximity.

Message Format Comparison

The protocol wire formats reveal the design philosophy of each:

The most significant difference is statefulness. MCP tool calls are fire-and-forget. You send a request, you get a response, there is no ongoing relationship. A2A tasks are persistent objects. They are created, they transition through states, they produce artifacts, and they can be cancelled or queried for history.

This matters for long-running agent tasks. A research task that takes 5 minutes cannot be expressed as a single synchronous MCP tool call, as the connection would time out. In A2A, you submit the task, receive an ID, and poll or subscribe for completion. The agent can run asynchronously, checkpointing its state, and deliver results when ready.

Transport and Authentication

MCP Transport

MCP supports two transports:

• stdio: The MCP server runs as a subprocess. The host communicates via stdin/stdout pipes. No network, no auth. Trust is implicit because the host spawned the process.
• SSE/HTTP: The server runs as a web service. Clients connect to /sse for the event stream and POST to /messages for client-to-server messages. Auth is not defined in the MCP spec. Implementations vary (API keys in headers, OAuth tokens, or nothing).

The lack of standardized authentication in MCP is a known gap. Each implementation handles auth differently, which creates integration friction in multi-server environments.

A2A Transport

A2A uses standard HTTPS for all communication. Authentication is declared in the Agent Card and enforced per-request:

// Agent Card authentication declaration
"authentication": {
  "schemes": ["bearer"],
  "credentials": "https://accounts.example.com/.well-known/openid-configuration"
}

Clients obtain credentials via the specified OAuth provider and include them as standard HTTP Authorization: Bearer <token> headers. This maps directly to how enterprise services already handle auth, with no new auth infrastructure required.

A2A also defines push notification delivery via webhooks: the client registers a callback URL, and the server calls it when task state changes. This is important for long-running tasks where polling is inefficient.

Trust Models

The trust models of MCP and A2A reflect their intended deployment contexts.

MCP's trust model assumes that the connection itself implies authorization. When Claude Desktop spawns an MCP server, it trusts that server by virtue of having launched it. When a user connects Claude to a remote MCP server, they are granting that server the ability to register tools that the LLM will treat as trusted. There is no cryptographic verification, no capability scoping at the protocol level, and no revocation mechanism.

This model is appropriate for single-user local deployments where the user chooses which servers to connect. It is problematic for multi-tenant enterprise environments where server operators and users may not have aligned interests.

A2A's trust model assumes the network is hostile. Every request is authenticated. Capabilities are declared ahead of time in the Agent Card (discoverable, auditable). The OAuth-based auth model supports token scoping. A client can grant an agent access to specific capabilities without granting it all permissions the client holds.

This trust model difference is fundamental, not cosmetic. MCP was designed for developer tooling with an implicit trust relationship between the user and their tools. A2A was designed for enterprise multi-agent coordination across organizational boundaries where trust must be explicit and auditable.

For security-sensitive applications, if your agents cross organizational boundaries, handle regulated data, or operate in multi-tenant environments, A2A's explicit trust model is more appropriate than MCP's. If you are building developer tools or single-user local integrations, MCP's simplicity is a feature.

Where They Overlap (and Where They Don't)

The question practitioners ask is "Can I use A2A to call tools instead of MCP?" Technically yes, but you would be building something MCP already provides more cleanly.

Where they overlap: Both protocols can be used to expose callable capabilities to an AI client. A2A's skills are conceptually similar to MCP's tools; both define what an agent/server can do. For simple synchronous capability exposure (call this function, get a result), either protocol works.

Where they do not overlap:

• Stateful long-running tasks: Only A2A. MCP has no task lifecycle.
• Cross-organizational trust: Only A2A. MCP's auth model is not designed for it.
• Agent capability discovery without a live connection: Only A2A (Agent Cards at well-known URLs). MCP requires connecting to discover tools.
• Multi-modal UX negotiation (text, audio, video, interactive forms): Only A2A.
• Local filesystem/process access: MCP's stdio transport does this cleanly. A2A requires HTTP, which is awkward for local tools.
• Integration with Claude's tool-use API: Currently only MCP. Claude natively understands MCP tool definitions; A2A requires wrapping.

The practical answer is that they are mostly complementary. An agent that uses MCP to access its local tools and A2A to delegate to remote agents is using both correctly.

Using A2A and MCP Together

A realistic multi-agent architecture for an autonomous SEO system might look like this:

Orchestrator Agent (Claude-based)
│
├── MCP Connections (local capabilities)
│   ├── filesystem-server (read/write local files)
│   ├── browser-server (web browsing, screenshot)
│   └── database-server (query local SQLite)
│
└── A2A Connections (remote specialist agents)
    ├── keyword-research-agent (A2A, handles long-running research tasks)
    ├── content-writing-agent (A2A, async content generation)
    └── publishing-agent (A2A, CMS integration with state tracking)

The orchestrator uses MCP for fast, synchronous local operations (read this file, query this database). It uses A2A to delegate long-running specialist tasks to remote agents: submitting tasks, tracking their state via the A2A task lifecycle, and receiving artifacts when complete.

How the two protocols interact at the message level

The orchestrator (an LLM with MCP tool access) is itself accessible via A2A. External clients delegate tasks to the orchestrator via A2A task submission. The orchestrator's internal operation uses MCP. The boundary between A2A and MCP is the boundary between the orchestrator's external interface and its internal tooling.

# Orchestrator: receive a task via A2A, fulfill it using MCP tools
async def handle_a2a_task(task: A2ATask) -> A2AArtifact:
    # Task received from a client agent via A2A
    goal = task.message.text  # e.g., "Research and write a post about BitNet"
 
    # Use MCP tools internally to research
    search_result = await mcp_client.call_tool("search_web", {
        "query": goal,
        "max_results": 10
    })
 
    # Delegate writing to a specialist A2A agent
    writing_task = await a2a_client.send_task(
        agent_url="https://writing-agent.example.com",
        message=f"Write a 3000-word post based on: {search_result.text}"
    )
 
    # Poll for completion
    result = await a2a_client.wait_for_task(writing_task.id)
 
    # Return the artifact via A2A
    return A2AArtifact(parts=[TextPart(text=result.artifacts[0].text)])

The pattern: A2A at the edges (how agents communicate with each other), MCP internally (how agents access their tools).

When to Use Which

The tiebreaker heuristics: if the capability is local and synchronous, use MCP. If it involves another autonomous agent, a long-running task, or a cross-organizational trust boundary, use A2A.

Maturity and Ecosystem (Early 2025)

MCP is approximately 6 months ahead of A2A in ecosystem development. As of early 2025:

• 1,000+ community-built MCP servers
• Official SDKs in Python and TypeScript
• Native integration with Claude Desktop and Claude API
• Third-party support in Cursor, Zed, Continue, and other dev tools
• Active server registries (no official one yet)

A2A launched with strong enterprise commitment but limited implementation tooling. As of early 2025:

• Specification published on GitHub
• Reference implementations in progress
• 50+ named enterprise partners (Salesforce, ServiceNow, Accenture, Deloitte) with stated intent to implement
• No official SDK yet (community TypeScript implementations exist)
• Google Cloud integration roadmap announced

The practical consequence is that if you are building today, MCP has more complete tooling and a larger ecosystem of ready-to-use servers. A2A is the right choice if you are building toward enterprise multi-agent coordination and are comfortable being early in the ecosystem.

Both protocols are under active development. The A2A specification is expected to mature significantly through 2025 as Google and its partners move from announcement to production. Watch the GitHub repository at github.com/google/A2A for specification updates.

Key Takeaways

• MCP (Anthropic) connects LLMs to tools and data sources (vertical integration). A2A (Google) connects AI agents to other AI agents (horizontal coordination). They solve different problems and compose naturally in multi-agent systems.

• The most significant technical difference is statefulness. MCP tool calls are stateless request/response. A2A tasks are persistent objects with a lifecycle (submitted → working → completed/failed), supporting long-running async operations.

• A2A uses explicit credential-based authentication (OAuth 2.0) declared in Agent Cards. MCP's authentication is not standardized in the core protocol; trust is host-based. A2A is the right choice for cross-organizational agent communication.

• Agent Cards (A2A) enable capability discovery without a live connection, via /.well-known/agent.json. MCP requires an active connection to list tools. There is no equivalent static discovery mechanism.

• The natural architecture for complex agent systems uses MCP internally (agent to tools) and A2A externally (agent to agent). The boundary between the two protocols is the boundary between an agent's internal capabilities and its external interfaces.

• MCP has a 6-month head start in ecosystem maturity with 1,000+ community servers and native Claude integration. A2A has stronger enterprise backing (50+ partners) but is earlier in tooling development as of early 2025.

FAQ

What is the difference between A2A and MCP?

MCP (Model Context Protocol, from Anthropic) defines how an LLM connects to external tools and data sources: it is the interface between a model and its capabilities. A2A (Agent2Agent, from Google) defines how AI agents communicate with and delegate tasks to other AI agents: it is the interface between agents. MCP interactions are synchronous tool calls (request a result, receive it immediately). A2A interactions are asynchronous task delegations (submit a goal, receive artifacts when complete). They are complementary protocols: most sophisticated agent systems need both.

Can A2A replace MCP?

No. A2A and MCP address different integration layers. A2A can technically expose callable capabilities (similar to MCP tools), but it is designed for agent-to-agent coordination with stateful task lifecycles and cross-organizational authentication, making it overengineered for simple tool calls. MCP cannot replace A2A for long-running async tasks, cross-organizational trust, or Agent Card discovery. The right architecture for most multi-agent systems is both: MCP for tool access within an agent, A2A for coordination between agents. Choosing one to replace the other creates either unnecessary complexity or missing capabilities.

What is an Agent Card in A2A?

An Agent Card is a JSON document served by an A2A-compatible agent at the standard URL /.well-known/agent.json, describing what the agent can do and how to interact with it. It includes the agent's name, description, supported skills (capabilities), accepted input and output modalities (text, audio, video, data), streaming and push notification support, and authentication schemes. Agent Cards enable capability discovery without requiring a live connection or pre-configured integration. Any client can query the well-known URL to learn what an agent offers before deciding whether to interact with it.

Which protocol should I use for building AI agents in 2025?

Use MCP for connecting your agent to local tools, APIs, databases, and data sources. The Claude API has native MCP support, the ecosystem has 1,000+ servers, and the tooling is mature. Use A2A when your agents need to delegate tasks to other autonomous agents, especially across organizational boundaries or for long-running async work. If you are building a single-agent application that needs tool access, MCP is sufficient. If you are building a multi-agent orchestration system or an enterprise platform where agents from different teams or companies need to collaborate, add A2A at the agent-to-agent layer.

Does Claude support A2A?

As of early 2025, Claude natively supports MCP for tool integration but does not have native A2A support. Claude can participate in A2A workflows as an agent. You can build an A2A server wrapper around Claude that exposes Claude's capabilities as A2A skills and accepts task delegations. Anthropic and Google have both indicated intent for protocol interoperability, and the open-source community has published A2A client libraries that can be used alongside the Claude API. Native A2A support in Claude's official clients is expected but not publicly announced as of March 2025.

The agent protocol landscape is consolidating faster than most people expected. Six months after MCP's launch and weeks after A2A's announcement, the rough architecture of the agentic web is becoming visible: MCP as the tool layer, A2A as the coordination layer, with the two protocols composing naturally at the boundary between an agent's internal operation and its external interfaces.

The practical guidance is straightforward. Build MCP servers for capabilities you want to expose to LLMs. Build A2A agents for capabilities you want to expose to other agents. Use both if you are building an orchestrator that needs tools and participates in multi-agent workflows.

The ecosystem for both protocols is early. This is an opportunity. The developers who understand the protocols deeply (not just "what they are" but how their trust models, message formats, and state management differ) will be the ones who build the multi-agent infrastructure that everything else runs on.

The specs are public and readable. MCP is at modelcontextprotocol.io. A2A is at github.com/google/A2A. Both are short enough to read in an afternoon. Read them.