A2A vs MCP: agent-to-agent vs agent-to-tool protocols

The debate around a2a vs mcp usually starts from a false premise: that they compete. They solve adjacent but distinct problems. MCP gives a model or agent a clean, typed interface to tools and data sources. A2A gives independent agents a way to discover each other and delegate work over HTTP. If you are building an LLM system, you will likely touch both, but for different layers of the stack.

What each protocol actually solves

MCP (Model Context Protocol) is a local-first RPC contract. An MCP client—typically embedded in an agent loop or IDE—talks to an MCP server over stdio or HTTP+SSE using JSON-RPC 2.0. The server exposes three primitive surfaces: tools (callable functions), resources (readable data), and prompts (templated messages). It is the cheapest way to give a model a search_sql or send_slack capability without hand-rolling JSON schemas in your prompt.

A2A (Agent-to-Agent) is a coordination protocol. An agent publishes an “Agent Card” at a well-known URL describing its skills and endpoint. Another agent sends it a task containing a message; the remote agent processes it asynchronously and returns artifacts. A2A assumes the other party is an autonomous system with its own context, not a dumb function.

Wire format and transport

MCP is strict JSON-RPC. A tool call looks like:

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "add",
    "arguments": {"a": 2, "b": 3}
  }
}

A minimal Python server using the official SDK:

from mcp.server.fastmcp import FastMCP

mcp = FastMCP("demo")

@mcp.tool()
def add(a: int, b: int) -> int:
    return a + b

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

A2A rides on plain HTTP POSTs with JSON payloads. Discovery is via a static card:

{
  "name": "WeatherAgent",
  "url": "https://weather-agent.example.com",
  "capabilities": {"streaming": true},
  "skills": [{"id": "get_forecast", "name": "Get Forecast"}]
}

And a task handoff:

POST /tasks/send
{
  "taskId": "t-001",
  "message": {"role": "user", "parts": [{"text": "forecast for SF"}]}
}

Capabilities

MCP is synchronous by default. Your agent calls a tool and blocks until it returns a result. It supports streaming via SSE for resource updates, but the mental model is “function call.” MCP also defines a sampling method where the server can ask the client to run a model inference—useful for recursive tool chains, though rarely used.

A2A is built for long-running, stateful work. A task has a lifecycle (submitted, working, input-required, completed). It supports push notifications, artifact streaming, and multi-turn negotiation. If the remote agent needs more input, it responds with input-required rather than failing.

The a2a vs mcp capability gap is really about autonomy: MCP tools do not negotiate, A2A agents do.

Latency and throughput

A local MCP server over stdio adds microseconds of serialization overhead. Remote MCP over HTTP is one network round-trip plus model time. Throughput is bounded by your agent loop, not the protocol.

A2A inserts at least one extra network hop between agents, often more if a task is delegated recursively. Because tasks are async, you pay a polling or webhook cost. For a tight ReAct loop calling calculator, A2A would be absurd. For a 30-second document synthesis spanning three specialized agents, A2A’s overhead is invisible.

If your MCP server forwards LLM calls to a gateway, n4n.ai offers a single OpenAI-compatible endpoint across 240+ models with automatic fallback, which keeps client code unchanged when a provider degrades.

Cost model

Neither protocol charges per call—they are specs. The cost is infrastructure. MCP servers are usually sidecars or lambda functions; you pay for compute and any downstream API the tool hits. A2A agents are full services with availability requirements, so you pay for idle uptime and orchestration plumbing.

Do not pick A2A to avoid MCP server hosting. You will host more, not less.

Ergonomics

MCP SDKs in Python and TypeScript are mature. You decorate a function and get schema inference for free. Debugging is local: stderr logs, curl against the socket.

A2A forces you to implement a task state machine, parse agent cards, and handle partial failures. The spec is newer; reference clients exist but the happy path is longer. If you just want get_user(123), MCP is 10 lines. A2A is 100 lines of task wrapping.

Ecosystem

MCP has a public registry of servers (filesystem, postgres, slack) and first-class support in Claude Desktop, IDE extensions, and LangChain. It won because tool use is the immediate pain.

A2A is backed by Google and a consortium of enterprise vendors. Adoption is early; you will write both ends of the wire more often than not. Its value shows in multi-vendor agent meshes where no single party controls the tool layer.

Limits

MCP has no discovery mechanism. An agent must be configured with server URLs ahead of time. It also assumes a single trusted client; there is no built-in agent identity or delegation.

A2A deliberately omits fine-grained tool schemas. You cannot cleanly express “this agent exposes a typed refund_payment(amount_usd) function” the way MCP does. It expects natural-language or loosely structured messages.

Comparison table

Dimension	MCP	A2A
Primary purpose	Agent-to-tool / model-to-data	Agent-to-agent delegation
Transport	stdio, HTTP+SSE (JSON-RPC 2.0)	HTTP POST + JSON, Agent Card discovery
Interaction model	Synchronous function call	Async task with state machine
Discovery	None (static config)	Well-known Agent Card URL
Typed inputs	Yes (JSON Schema)	Loose message parts
Streaming	SSE for resources	Task artifacts, push notifications
Auth model	Inherits transport (local or bearer)	Agent identity in card, bearer per task
Ecosystem maturity	High, many public servers	Early, enterprise-driven
Best for	Tight agent loops, tool calls	Cross-team multi-agent workflows

Which to choose

Single agent, needs tools
Use MCP. Wrap your internal APIs as MCP servers and point your agent at them. You get typed schemas, low latency, and zero negotiation overhead.

Multiple agents in one codebase
Still MCP for the tool layer. Orchestrate the agents in-process with a supervisor pattern. A2A adds network boundaries you do not need.

Cross-organization agent mesh
Use A2A. When the remote party is a different team’s deployment with its own rate limits and policies, agent cards and task lifecycle save you from brittle hardcoded endpoints.

Hybrid (common)
MCP inside each agent for its tools; A2A between agents for handoffs. A weather agent exposes get_forecast via MCP to its own loop, but advertises “WeatherAgent” via A2A to the planning agent. This split matches the a2a vs mcp boundary exactly: tools are local, agents are remote.

Pick based on trust boundary, not hype. If you control the code, MCP. If you control only the contract, A2A.