MCP vs A2A: A Technical Deep Dive for AI Agent Builders

Two protocols. Two philosophies. One ecosystem. Here's the engineering breakdown you need to choose the right tool — and why the best agents use both.

The Two Protocols, Summarized

Before we dive into packet structures and integration patterns, let's establish the core distinction:

Both are open protocols. Both use JSON-RPC as the transport layer. Both are designed for the age of AI agents. But they solve fundamentally different problems.

MCP Under the Hood

Architecture

┌─────────────┐     JSON-RPC      ┌──────────────┐
│  AI Agent    │ ◄──────────────► │  MCP Server  │ ──► Tool/API
│  (Host)      │   stdio / SSE    │  (Process)   │
└─────────────┘                   └──────────────┘

An MCP Host (your AI agent) connects to an MCP Server (a tool provider). The server exposes three primitives:

1. Tools — Functions the agent can call (e.g., send_email, query_database)
2. Resources — Data the agent can read (e.g., files, database records)
3. Prompts — Reusable prompt templates the agent can invoke

Transport Layer

MCP supports two transport mechanisms:

// Option 1: stdio (local, subprocess-based)
const transport = new StdioClientTransport({
  command: "node",
  args: ["./my-mcp-server.js"]
});

// Option 2: SSE (remote, HTTP-based)
const transport = new SSEClientTransport({
  uri: "https://api.example.com/mcp/sse"
});

When to use which:

• stdio: Local tools, low latency, no network overhead. Ideal for file operations, local databases, CLI tools.
• SSE: Remote APIs, cloud services, multi-tenant tools. Ideal for SaaS integrations, third-party APIs.

Message Flow

// Agent → Server: Initialize
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "initialize",
  "params": {
    "protocolVersion": "2024-11-05",
    "capabilities": {},
    "clientInfo": { "name": "my-agent", "version": "1.0.0" }
  }
}

// Server → Agent: Response with capabilities
{
  "jsonrpc": "2.0",
  "id": 1,
  "result": {
    "protocolVersion": "2024-11-05",
    "capabilities": {
      "tools": { "listChanged": true }
    },
    "serverInfo": { "name": "email-sender", "version": "1.2.0" }
  }
}

// Agent → Server: Call a tool
{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "tools/call",
  "params": {
    "name": "send_email",
    "arguments": {
      "to": "client@example.com",
      "subject": "Project Update",
      "body": "Here's the latest status..."
    }
  }
}

Error Handling

MCP defines standard error codes:

// Standard JSON-RPC errors
ServerErrorCode = {
  PARSE_ERROR: -32700,
  INVALID_REQUEST: -32600,
  METHOD_NOT_FOUND: -32601,
  INVALID_PARAMS: -32602,
  INTERNAL_ERROR: -32603,
}

// Custom application errors
throw new McpError(
  ErrorCode.InvalidParams,
  "Missing required field: 'to'"
);

SkillExchange MCP Integration

When you publish an MCP skill on SkillExchange, the platform handles the server lifecycle:

# Your skill code (what you write)
from skillexchange import Skill

@skill.tool(name="translate", description="Translate text between languages")
async def translate(text: str, source: str, target: str) -> dict:
    result = await translation_api(text, source, target)
    return {"translated": result, "confidence": 0.97}

# SkillExchange handles: hosting, discovery, auth, billing, monitoring

A2A Under the Hood

Architecture

┌──────────────┐                    ┌──────────────┐
│  Agent A      │ ◄────────────────► │  Agent B      │
│  (Client)     │    A2A Protocol    │  (Server)     │
│               │                    │               │
│  - AgentCard  │    JSON-RPC/HTTP   │  - AgentCard  │
│  - Tasks      │ ◄────────────────► │  - Tasks      │
│  - Messages   │                    │  - Artifacts  │
└──────────────┘                    └──────────────┘

A2A introduces a different mental model. Instead of tools, you deal with Agent Cards — structured descriptions of what an agent can do.

Agent Cards: The Discovery Layer

{
  "name": "research-agent",
  "description": "Deep research agent that produces comprehensive reports",
  "url": "https://research.example.com/a2a",
  "capabilities": {
    "streaming": true,
    "pushNotifications": true
  },
  "skills": [
    {
      "name": "market-research",
      "description": "Conduct market research on any industry or topic",
      "inputSchema": {
        "type": "object",
        "properties": {
          "topic": { "type": "string" },
          "depth": { "enum": ["quick", "standard", "deep"] }
        }
      }
    }
  ],
  "authentication": {
    "schemes": ["bearer"]
  }
}

Task Lifecycle

A2A is fundamentally task-oriented, not request-response:

1. Client sends task → Server creates task (status: SUBMITTED)
2. Server begins work → Task status: WORKING
3. Server sends messages → Task receives incremental updates
4. Server completes → Task status: COMPLETED
5. Client retrieves artifacts → Final deliverables

# Agent A initiates a task with Agent B
task = await a2a_client.send_task(
    agent_url="https://research.example.com/a2a",
    task={
        "id": "task-123",
        "message": {
            "role": "user",
            "parts": [{"type": "text", "text": "Research the MCP tooling market"}]
        }
    }
)

# Stream updates as the research agent works
async for event in a2a_client.subscribe(task.id):
    if event.type == "task_update":
        print(f"Status: {event.status}")
    elif event.type == "artifact":
        print(f"Deliverable: {event.artifact}")

Key Differences from MCP

When to Use Which: Decision Framework

Use MCP When:

✅ Your agent needs to call a specific function (send email, query DB, read file) ✅ The interaction is synchronous — call and get a result ✅ You're building a tool that many agents will use ✅ You need low latency and simple integration ✅ The task can be completed in a single exchange

Example use cases:

• Database queries
• File operations
• API integrations (Stripe, GitHub, Slack)
• Data transformation
• Image/speech processing

Use A2A When:

✅ Your agent needs to collaborate with another agent over multiple turns ✅ The task is complex, multi-step, or requires planning ✅ You need to delegate autonomous work to a specialist ✅ The interaction is long-running with intermediate updates ✅ You want agents to negotiate capabilities and terms

Example use cases:

• Research and report generation
• Multi-agent project management
• Negotiation and procurement
• Code review and quality assurance
• Complex data analysis pipelines

Use Both When:

✅ You're building a production agent system that does real work

# A production agent uses both
class ProductionAgent:
    def __init__(self):
        # MCP tools for atomic operations
        self.db = mcp.connect("database-query-skill")
        self.email = mcp.connect("email-sender-skill")
        self.stripe = mcp.connect("stripe-payment-skill")
        
        # A2A peers for complex collaboration
        self.researcher = a2a.connect("research-agent")
        self.reviewer = a2a.connect("code-review-agent")
    
    async def handle_client_request(self, request):
        # Delegate research to A2A peer
        research = await self.researcher.assign_task(
            topic=request.topic, depth="deep"
        )
        
        # Use MCP tools for specific operations
        data = await self.db.query(research.data_query)
        
        # Delegate review to A2A peer
        review = await self.reviewer.assign_task(
            content=research.report, criteria="factual-accuracy"
        )
        
        # Send results via MCP email tool
        await self.email.send(
            to=request.client_email,
            subject=f"Research: {request.topic}",
            body=review.final_report
        )

Performance Comparison

Note: A2A's higher latency is by design — it's optimized for complex, multi-turn interactions, not microsecond function calls.

Building for SkillExchange: Which Protocol?

SkillExchange currently focuses on MCP skills — and for good reason:

1. Lower barrier to entry: A tool is simpler to build than an agent
2. Clearer value proposition: "Pay $0.002 per database query" vs. "Pay $X for a multi-agent workflow"
3. Composability: Agents compose MCP tools into workflows; A2A agents are the composition

The recommended architecture:

[Your Agent] ──MCP──► [SkillExchange Skills]
                │
                └──A2A──► [Other Agents]

Use SkillExchange MCP skills as building blocks. Use A2A when you need peer collaboration between agents.

The Future: Convergence

Both protocols are evolving rapidly. Key trends to watch:

1. MCP is adding streaming support — blurring the line with A2A's async model
2. A2A is adding tool primitives — allowing agents to expose tools directly
3. SkillExchange will support both — unified discovery for tools and agents

The smartest builders aren't picking sides. They're building protocol-agnostic skills that work across both.

// Future SkillExchange skill that works with both protocols
export default new Skill({
  name: "data-enrichment",
  // MCP interface for direct tool calls
  mcp: {
    tools: [{ name: "enrich", handler: enrichData }]
  },
  // A2A interface for agent delegation
  a2a: {
    skills: [{ name: "enrich-dataset", handler: enrichDatasetTask }]
  }
});

TL;DR for Builders

Don't overthink it. Start with MCP. Build a tool. Publish it on SkillExchange. Earn from every call. When you need agent collaboration, add A2A.

Ready to Build?

👉 Publish your first MCP skill — From zero to earning in 30 minutes.

👉 Read the MCP docs — Complete integration guide with code examples.

👉 Explore A2A integration — Early access for agent builders.

The best agents don't choose between protocols. They choose the right tool for the job — and SkillExchange gives them access to both.