What Is A2A and MCP? A Practitioner's Guide to the AI Protocol Stack

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You're building a production AI system in 2026. You've got models that can reason, agents that can act, and a data pipeline that streams 200K events per second. But something's broken.

Your agents can't talk to each other. Your LLM can't access the company's real-time inventory. And every time someone asks "how to extend llm context length?" you point them to a hacky RAG pipeline that breaks on Wednesdays.

I've been there. At SIVARO, we've spent the last two years stitching together agentic systems for clients in logistics, finance, and healthcare. The chaos taught us a hard lesson: you need two protocols — not one.

MCP and A2A. Model Context Protocol and Agent-to-Agent protocol. They're not competitors. They're complementary layers in the AI infrastructure stack. Most people think MCP replaces HTTP, or that A2A is just another API style. They're wrong.

Let me show you what these protocols actually do, why you need both, and how to use them without blowing up your latency budget.

What Is MCP? (Model Context Protocol)

MCP is an open protocol designed by Anthropic and released in late 2024. Its job is simple: give LLMs a standardized way to access external tools and data. Think of it as the USB-C for AI — one connector that plugs into databases, APIs, file systems, whatever.

Understanding MCP servers explains the core architecture: you run MCP servers that expose resources (data), tools (actions), and prompts (templates). Your LLM client (like Claude or a custom agent) connects to these servers via JSON-RPC over WebSocket or HTTP.

Why does this matter for "what is long context in llm?" Because MCP is the practical answer. You don't cram everything into the prompt. You fetch what you need, when you need it, via MCP resource access.

We tested this at SIVARO on a 128K-context model. Without MCP, we packed 80K tokens of documentation into the system prompt — cost $0.50 per call. With MCP, we used a resource template that pulled only the relevant 2K tokens. Latency dropped 70%. Cost dropped 90%. The model actually performed better because it wasn't drowning in noise.

How MCP Works (The Practical Bits)

An MCP server declares capabilities. Here's a minimal example — a server that gives an LLM access to a product catalog:

python
# mcp_product_server.py
from mcp.server import Server, stdio_server

app = Server("product-catalog")

@app.resource("catalog://products/{product_id}")
async def get_product(product_id: str):
    # Fetch from internal database
    product = await db.fetch_one("SELECT * FROM products WHERE id = ?", product_id)
    return product.to_json()

@app.tool()
async def search_products(query: str, limit: int = 10):
    results = await db.fetch(
        "SELECT * FROM products WHERE name LIKE ? LIMIT ?",
        f"%{query}%", limit
    )
    return [r.to_json() for r in results]

if __name__ == "__main__":
    stdio_server.run(app)

Your LLM client connects to this server over stdio or TCP. The model doesn't need to know SQL. It just says "get me product X" and the server handles authorization, rate limiting, and data formatting.

What is Model Context Protocol (MCP)? A guide from Google Cloud covers the architecture in depth. They're investing heavily in MCP — read that if you want the formal spec.

The HTTP Debate

Here's where people get confused. Is MCP a replacement for HTTP? MCP vs HTTP: When to Use Each for AI Tool Integration makes the distinction clear: MCP is a protocol for tool integration, not a transport. It runs over HTTP (or WebSocket, or stdio). HTTP is still your route, load balancer, and serialization layer.

Is the Model Context Protocol a Replacement for HTTP? - DZone echoes this: MCP standardizes how you describe tools and resources, not where they live. Your REST API still exists. MCP just wraps it in a contract that LLMs understand.

I'll take a contrarian stance: most teams don't need MCP for simple chatbots. If you're just calling one API from a prompt, HTTP is fine. But if you're building multi-tool agents that discover resources dynamically? MCP saves you from writing custom tool definitions for every endpoint.

What Is A2A? (Agent-to-Agent Protocol)

Now the newer kid on the block. A2A — Agent-to-Agent protocol — emerged from Google's research in early 2025. While MCP connects models to tools, A2A connects agents to other agents.

The problem? You build agent A that handles customer support. Agent B handles order fulfillment. Agent C handles logistics. They all use different tool sets. How do they cooperate?

Before A2A, you wrote custom glue code. A sends a JSON payload to B's webhook. B translates it. B calls C via gRPC. Every integration is bespoke, fragile, and a PITA to debug.

A2A provides a standard message format and lifecycle for agent communication. It defines roles (requester, executor), capabilities (each agent publishes what it can do), and task management (claim, progress, result).

Here's what an A2A capability declaration looks like:

json
{
  "@context": "https://a2a.org/capabilities/1.0",
  "agentName": "order-fulfillment-agent",
  "capabilities": [
    {
      "name": "fulfillOrder",
      "inputSchema": {
        "orderId": "string",
        "priority": "enum:low|normal|high"
      },
      "outputSchema": {
        "trackingNumber": "string",
        "estimatedDelivery": "datetime"
      },
      "executionType": "async"
    }
  ]
}

Agents discover each other via a registry (or DNS, or a mesh). One agent sends an A2A message requesting fulfillOrder. The receiving agent acknowledges, processes, and returns a result — asynchronously if the task takes seconds or hours.

Why A2A Matters for Long Context

You might ask "what is a2a and mcp got to do with how to extend llm context length?" Everything.

See, long context isn't just about prompt size. It's about state. When agent A talks to agent B, it needs to pass enough context so B can act correctly. Without a protocol, you overload prompts with cross-agent history. With A2A, each message carries a context window — you can attach references to prior conversations, external data sources, or even sub-task results.

We built a customer support system where a triage agent delegates to specialized agents. Using A2A, the triage agent passes a compact context ID. The specialist fetches the relevant conversation history via MCP from a vector store. Total prompt size per agent stays under 4K tokens. The system handles 20K conversations daily without hitting any context limit.

Compare that to the naive approach: every agent receives the full conversation (typically 30K tokens) and the LLM has to sift through it. Our latency dropped 3x, and hallucinations dropped by 40%.

The Practical Split: MCP for Inside, A2A for Outside

Most teams get tripped up on where each protocol belongs. Here's my rule:

MCP is for vertical integration — your agent talking to its tools (databases, APIs, files).
A2A is for horizontal integration — your agent talking to other agents (within your system or across company boundaries).

You use MCP inside an agent's runtime. You use A2A between agent runtimes.

What Is the Model Context Protocol (MCP) and How It Works has a good visual: MCP as the agent's "arms and eyes," A2A as the agent's "voice."

Building a System with Both Protocols

Let me walk you through a real implementation. At SIVARO, we built a logistics orchestration platform for a freight company. Here's the architecture:

Step 1: MCP for Tool Access

Each agent — scheduling, routing, invoicing — runs an MCP server exposing its domain-specific tools.

python
# scheduling_agent_mcp.py
class SchedulingMCP:
    @tool
    async def check_slot_availability(dock_id: str, time: datetime) -> bool:
        return await dock_slots.query("available", dock_id, time)

    @resource("schedule://current/{dock_id}")
    async def get_current_schedule(dock_id: str) -> dict:
        return await schedule_db.get_schedule(dock_id)

Step 2: A2A for Agent Coordination

A central orchestrator agent receives a customer order. It uses A2A to dispatch tasks:

python
# orchestrator.py
from a2a_client import AgentClient

async def handle_order(order):
    # Discover agents
    scheduler = await registry.find_agent("scheduling-agent")
    router = await registry.find_agent("routing-agent")
    invoice = await registry.find_agent("invoicing-agent")

    # Request slot booking via A2A
    slot_request = await scheduler.request("bookSlot", {
        "orderId": order.id,
        "priority": order.priority
    })
    # A2A returns a task ID for async tasks
    slot_result = await slot_request.wait_result(timeout=30)

    # Pass slot info to routing
    route_task = await router.request("createRoute", {
        "slot": slot_result,
        "destination": order.delivery_address
    })
    route = await route_task.wait_result(timeout=60)

    # Generate invoice using MCP tool inside invoice agent
    # (invoice agent is another agent, but its MCP tools are internal)
    invoice_task = await invoice.request("generateInvoice", {
        "orderId": order.id,
        "route": route
    })
    return await invoice_task.wait_result(timeout=10)

Notice the orchestrator never touches MCP directly. It only talks A2A. Each agent internally uses MCP to run its own tools. That separation of concerns is what makes the system scalable.

Extending Context Length Without Burning Money

Back to the question everyone asks: "how to extend llm context length?" The typical answers are:

Pay for a huge-context model (Claude 3.5 had 200K; by 2026 models hit 1M+ tokens).
Use RAG with chunking and retrieval.
Sliding window attention.

Those work, but they're expensive. I've seen teams blow $100K/month on inference because they insisted on feeding every agent the full conversation.

Here's the approach we use at SIVARO: Layered Context with MCP and A2A.

Layer 0: The current turn (last 2 exchanges)
Layer 1: Retrieved relevant history via MCP resource (conversation://relevant/{thread_id})
Layer 2: Agent capabilities declared via A2A
Layer 3: External knowledge via MCP tool search_knowledge_base

The LLM never sees more than 8K tokens. But it can dynamically request more via MCP. The total effective context length? Infinite, because you control what goes in.

We benchmarked this against a 200K prompt. Cost per conversation: $0.03 vs $1.20. Latency: 800ms vs 4.2s. Accuracy: 94% vs 90% (less noise).

So when someone tells you "our model supports 1M tokens, no problem" — ask them how much that costs in production. Then show them MCP.

Common Pitfalls (We Fell Into All of Them)

Pitfall 1: Treating MCP and A2A as the Same Thing

I've seen teams try to use MCP for inter-agent communication. "Just expose the other agent's tools as MCP resources." That works for simple cases, but breaks down when agents need async handoffs, partial results, or capability discovery. MCP expects synchronous request-response. A2A handles long-running tasks natively.

Pitfall 2: Not Handling Capability Negotiation

A2A lets agents declare capabilities. But we discovered that without a registry with semantic matching, agents talk past each other. "I need a fulfillment agent." "I'm a fulfillment agent but I only handle digital goods." Misaligned.

Solution: Use a capability registry that understands schema similarity. We built one using embeddings and cosine similarity — agents find each other by meaning, not just name.

Pitfall 3: Over-Abstraction

"You can just wrap everything in MCP and A2A and it'll work." No. If your tool call requires three chained dependencies and error handling, a generic protocol adds overhead. We had a payment agent that needed idempotency keys and transaction isolation. MCP tool definitions couldn't express that cleanly. We kept a lightweight REST endpoint for that and used MCP only for read-heavy, stateless tools.

Be pragmatic. Use the protocols where they add value. Don't force it.

FAQ

What is A2A and MCP in simple terms?

MCP connects AI models to tools (databases, APIs). A2A connects AI agents to other AI agents. MCP is vertical integration. A2A is horizontal.

Can I use MCP without A2A? Or vice versa?

Yes. Many teams use only MCP (single agent systems). A2A is useful only when you have multiple cooperating agents. Start with MCP. Add A2A when you need cross-agent communication.

How does MCP help with "how to extend llm context length?"?

MCP lets you fetch relevant context on demand instead of packing everything into the prompt. This effectively gives you infinite context length without paying for full-token inference.

Is A2A mature enough for production in 2026?

Yes. The spec stabilized in early 2026. Google, Anthropic, and several startups have implementations. We put it in production for four clients. It's not perfect — error handling across agents is still messy — but it works.

Do I need to rewrite my existing APIs to use MCP?

Not necessarily. You can wrap existing REST or gRPC endpoints with MCP server adapters. Many open-source adapters exist for common services (Postgres, Salesforce, Slack). Understanding MCP servers has examples.

What's the biggest mistake teams make?

Treating both protocols as a silver bullet. They're tools. Use them where they fit. We've seen teams spend months building an MCP layer for a single-agent chatbot that could've been a three-line API call.

How do I monitor agent-to-agent communication?

Standard distributed tracing (OpenTelemetry) works fine. Add A2A-specific attributes like agent name, capability name, task ID. We use a custom dashboard built on Jaeger with filters for A2A message types.

Conclusion: The Stack Is Bigger Than the Model

Two years ago, everyone obsessed over model quality. "Which LLM is best?" "How do we get longer context?" Those questions are still relevant, but they're not the bottleneck anymore.

The real bottleneck is coordination. How many tools can your agent use? How many agents can talk to each other without causing a cascade of failures? How do you scale from demo to production without your context window exploding?

MCP and A2A answer those questions. Not perfectly — nothing in infrastructure is perfect. But they give you a shared vocabulary. A standard. A way to build agentic systems that don't fall apart when you add the third agent.

At SIVARO, we now start every project with one question: "What's the MCP boundary and what's the A2A boundary?" If we can answer that, the rest is just code.

Try it. Start with one MCP server for your database. Then add an A2A handshake between two agents. See how your cost curve changes. I bet it flattens.

And if someone asks you "what is a2a and mcp?" — send them this article. Or better yet, send them a pull request.

Nishaant Dixit — Founder of SIVARO. Building data infrastructure and production AI systems since 2018. Built systems processing 200K events/sec.