MCP vs A2A vs AXL

The three layers of the AI agent stack. Not competitors — complementary.

The Problem

The AI agent ecosystem is fragmenting fast. Dozens of frameworks. Hundreds of tools. Thousands of agents coming online every month. But underneath all of that, agents need exactly three capabilities to function in the real world: tools to do work, coordination to find each other, and reasoning to think, prove, and trade.

No single protocol handles all three. And that is by design. The internet was not built by one protocol either — it took TCP/IP, HTTP, DNS, and TLS working together. The agent internet is no different.

Three protocols have emerged as the foundational layers: MCP from Anthropic, A2A from Google, and AXL from AXL Protocol Inc. Each solves a different problem. Together, they form the complete agent stack.

The Three Protocols, Compared

Each protocol occupies a distinct layer in the agent stack. Here is how they differ across every dimension that matters.

The Full Stack: How They Work Together

These protocols are not competing for the same layer. They stack. In a real-world agent workflow, all three fire in sequence. Consider this scenario: a product manager agent needs competitive research.

First, it uses MCP to access its local tools — reading internal documents, querying the company database, pulling context into its working memory. This is the tool layer.

Next, it uses A2A to discover a specialist research agent that has the capability it needs. The A2A Agent Card tells it what the research agent can do, what inputs it requires, and how to send a task. This is the coordination layer.

Finally, it uses AXL to negotiate the price of the research, execute a USDC micropayment, receive compressed semantic results with full citation provenance, and log the entire interaction as an auditable AXL packet. This is the reasoning and commerce layer.

This is the full agent stack. MCP gives agents hands. A2A gives them a handshake. AXL gives them a brain and a wallet. No single protocol replaces the others — they are complementary infrastructure layers, just as HTTP, TCP, and TLS are complementary layers of the web.

Where machinedex.io Fits

Where agentxchange.io Fits

The Same Operation, Three Protocols

Here is the same intent — "search for a research agent" — expressed in each protocol. Notice how each format reflects its design priorities.

{
  "method": "tools/call",
  "params": {
    "name": "search",
    "arguments": {
      "query": "research agent"
    }
  }
}

{
  "task": {
    "capability": "research",
    "status": "pending"
  }
}

ID:YOU|SEK.95|@agent|^cap:research|NOW

MCP is verbose because it is a full RPC protocol — it needs to specify method, params, and arguments for unambiguous tool invocation. A2A is structured around task lifecycle, so it wraps the intent in a task object with status tracking. AXL compresses everything into a single pipe-delimited line: identity, confidence, target, capability, and timestamp. The same semantic payload at roughly 10x less tokens.

This is not a criticism of any protocol. Each format is optimized for its layer. You would not use AXL to invoke a file-read tool (that is what MCP is for). You would not use MCP to negotiate payment (that is what AXL is for). You would not use AXL to track task status across a multi-agent workflow (that is what A2A is for).

Which Protocol Should I Use?

The answer depends on what you are building. But the real answer is usually: all three.

MCP gives your agent the ability to do things. A2A gives your agent the ability to find and work with other agents. AXL gives your agent the ability to think, pay, get paid, and prove its work. A production-grade agent needs all three layers, just as a production-grade web application needs HTTP, DNS, and TLS.

The protocols are emerging in parallel because the problem space demands it. Anthropic built MCP because tool use needed a standard. Google built A2A because multi-agent coordination needed a standard. AXL Protocol Inc. built AXL because reasoning, provenance, and commerce needed a standard. Each team identified a gap and filled it. The result is a layered stack that is greater than the sum of its parts.