A2A vs MCP: agents collaborate, tools connect
A2A and MCP solve different layers of the same agent stack. MCP gives an agent access to capabilities. A2A lets that agent delegate work to another agent. Many production systems will use both.
Agent2Agent Protocol. An open protocol for interoperable communication and task delegation between agents.
Originated at Google and is now hosted by the Linux Foundation's Agentic AI Foundation (AAIF).
Official A2A 1.0 specificationModel Context Protocol. An open protocol for connecting AI applications to tools, data sources, and reusable context.
Created by Anthropic and now hosted by the Linux Foundation's Agentic AI Foundation (AAIF).
Official MCP specification| Dimension | A2A | MCP |
|---|---|---|
| Primary job | Lets independent agents discover capabilities, delegate work, and coordinate outcomes. | Lets an AI application connect to tools, resources, prompts, and external context. |
| Core relationship | Agent ↔ agent | AI host/client ↔ server providing capabilities |
| Discovery surface | Agent Card describes identity, interfaces, auth, capabilities, and skills. | Initialization and list operations expose server capabilities, tools, resources, and prompts. |
| Unit of work | Messages, artifacts, and stateful tasks delegated to another agent. | Tool calls and reads of resources or prompts inside an AI application workflow. |
| Interfaces / transports | A2A 1.0 supports JSON-RPC, HTTP+JSON, and gRPC protocol bindings. | MCP defines JSON-RPC messaging over transports such as stdio and Streamable HTTP. |
| Best fit | Cross-vendor agents that own their logic and need to collaborate without exposing internals. | Giving models and agents a consistent way to use software, data, and operational tools. |
Which one should you use?
Start from the boundary you are standardizing. If both boundaries exist, the protocols compose cleanly.
- Your agent needs access to a database, API, file system, or SaaS action.
- You are exposing reusable tools, resources, or prompts to multiple AI clients.
- The caller should orchestrate the workflow and consume individual capabilities.
- You need to delegate a goal to another autonomous or semi-autonomous agent.
- The remote agent should retain control of its own tools, memory, and execution plan.
- You need capability discovery, task status, messages, and returned artifacts.
Give specialist agents tools
A research agent can use MCP servers for search, databases, documents, or internal APIs.
Delegate between those agents
A coordinator can use A2A to discover that specialist, assign a task, follow its state, and receive artifacts.