A2A Protocol vs MCP: What’s the Difference and When to Use Each

The agent ecosystem now has two protocols competing for attention: Anthropic’s MCP and Google’s A2A. People keep asking which one wins. The honest answer is that they solve different problems and most production systems will end up using both.

What MCP Actually Is

The model context protocol (MCP) is a standard for connecting a model to tools and data. An MCP server exposes a set of tools (functions an agent can call), resources (read-only data the agent can fetch), and prompts (templates the agent can use). An MCP client, usually the agent or the host application running the agent, discovers what’s available and routes calls.

The mental model is simple: one model talking to many tools. A Claude Code session can connect to a filesystem MCP server, a GitHub MCP server, and a search MCP server at the same time. The model picks which tool to call based on the user’s request. The protocol handles capability discovery, schema validation, and the transport layer.

MCP shipped in late 2024, picked up steam through 2025, and is the de facto plug for tool access in 2026. The reason it spread: it solved an obvious pain point. Before MCP, every agent framework reinvented tool registration, and tool providers had to ship N integrations for N frameworks.

What A2A Is For

Google’s agent-to-agent (A2A) protocol takes a step up the stack. Instead of “model talks to tools,” A2A handles “agent talks to other agents.” An agent advertises its capabilities through an agent card, accepts work items called tasks, and reports progress or returns artifacts when done. The protocol is HTTP-based, transport-agnostic over JSON-RPC, and supports streaming via server-sent events for long-running tasks.

The motivating use case is delegation across organizational boundaries. A scheduling agent at company A needs to coordinate with a calendar agent at company B. A research agent kicks off a sub-task to a specialist data agent. Each side runs its own model, its own tools, its own auth. They negotiate work through a shared envelope rather than embedding each other’s tools.

A2A’s contribution is the protocol around the work itself: task IDs, lifecycle states, artifact passing, authentication between agents. That layer didn’t exist as a standard before A2A. People built it ad-hoc with REST endpoints and message queues.

The Boundary Between Them

The clearest way to think about it: MCP is for things that aren’t agents, A2A is for things that are.

A weather API doesn’t reason. A SQL database doesn’t plan. A vector store doesn’t make decisions about what to do next. These belong behind MCP. The model is in charge, the tool executes.

Another agent does reason. It has its own context, its own tools (probably exposed over MCP), and its own goals. When your agent delegates to it, you don’t want to think of it as a function call. You want to hand off a task, get notified when it finishes, and trust the other agent to figure out how. That’s A2A.

This split matters because the protocols make different assumptions. MCP assumes a quick request and response, with the model in the loop. A2A assumes work can take minutes or hours, that the work happens out of band, and that you might lose the connection and reconnect to poll for results.

Where They Overlap

The line gets blurry around a few cases.

Long-running tools (a deep research call that takes ten minutes, an image render that requires polling) sit awkwardly in pure MCP, which is happiest with sub-second responses. Some MCP servers handle this with job IDs and polling endpoints, but the ergonomics aren’t great. A2A’s task model is built for this.

Agents that wrap other agents are also fuzzy. If a tool provider runs an internal agent to handle a request (say, a “deep research” tool that runs a multi-step plan under the hood), it could expose itself as MCP (and hide the agent inside) or as A2A (and own that it’s an agent). Both are defensible.

In practice, the protocols compose. An agent uses MCP to access its own tools. The same agent uses A2A to talk to peer agents. The peer agent, in turn, uses MCP for its tools. The protocols don’t compete; they stack.

When Each One Wins

Use MCP when:

  • You need to give a model access to a capability (web search, email, image generation, database read, file write).
  • The capability is mostly deterministic and finishes quickly.
  • The model is the planner.

Use A2A when:

  • You need to delegate work across system boundaries (different teams, different vendors, different models).
  • The work can take a while and you need to track its lifecycle.
  • The other side is itself an agent making its own decisions.

Use both when: You’re building anything non-trivial. Real systems have both kinds of dependencies. The protocols were designed to coexist.

How AgentPatch Fits

AgentPatch is an MCP-first tool marketplace. It exposes web search, email, image generation, scrape, news, transcripts, financial data, and dozens of other tools through a single MCP endpoint. Your agent connects once and gets the catalog.

The reason MCP-first makes sense for a tool platform: tools are not agents. They run, they return data, they don’t have goals. MCP is the right level of abstraction for that, and standardizing on it means any MCP-compatible client (Claude Code, Cursor, Codex CLI, the Claude Agent SDK, OpenClaw) works without per-client integration.

A2A is on the roadmap for the cases where a tool actually is an agent under the hood, the deep research call, the long-running scrape that needs to crawl and follow links over an hour. For those, the task lifecycle in A2A is a better fit than long-poll MCP.

Wrapping Up

MCP and A2A solve different problems. MCP is for an agent talking to tools. A2A is for an agent talking to other agents. The protocols complement each other, and most production systems in 2026 will end up speaking both. If you need to give your agent access to a catalog of tools today, MCP is the place to start. Browse the AgentPatch catalog at agentpatch.ai.

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