A2A vs MCP for Agent Integrations
A2A and MCP solve different integration problems.
MCP connects an AI application to external systems: tools, files, databases, search, prompts, and workflows. An MCP server exposes capabilities, and an MCP client consumes them.
A2A connects agents to other agents. It focuses on capability discovery, task management, messages, artifacts, and collaboration across agents that may be built with different frameworks or vendors.
Use MCP when the agent needs tools or data
Choose MCP when your assistant needs to query a database, read files, call an internal API, use search, or expose a repeatable workflow to several AI clients. MCP is the integration boundary between the AI client and external capabilities.
Use A2A when one agent delegates to another agent
Choose A2A when a planning agent needs to ask a specialized agent to complete a task, monitor its status, and receive artifacts. A2A's Agent Card, task lifecycle, and message parts are designed for agent-to-agent coordination.
Use both in larger systems
In a realistic enterprise workflow, a coordinator agent might use A2A to delegate tax analysis to a finance agent. That finance agent might use MCP servers to access policies, spreadsheets, and approval workflows.
Security checklist
Authenticate the remote agent or MCP server, authorize capabilities by user and task, log every delegation and tool call, validate artifacts before using them, and avoid giving a remote agent broad credentials just because it is reachable through a protocol.
Sources
- Google Agent2Agent announcement: https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/
- MCP introduction: https://modelcontextprotocol.io/docs/getting-started/intro