A2A vs MCP: Two Protocols, Two Problems, Not Interchangeable
Last week an engineering lead asked me which protocol to pick for a new agent system: Anthropic's Model Context Protocol (MCP) or Google's Agent-to-Agent (A2A). Wrong question.
A2A vs MCP: Two Protocols, Two Problems, Not Interchangeable
Last week an engineering lead asked me which protocol to pick for a new agent system: Anthropic’s Model Context Protocol (MCP) or Google’s Agent-to-Agent (A2A). Wrong question. They solve different problems, and treating them as alternatives is why multi-agent prototypes stall at the pilot stage.
What each protocol actually does
MCP is a client-server contract between one agent and its tools. The agent is the client; a database, a file system, a Jira instance each expose themselves as MCP servers. The protocol standardises how an LLM discovers tool schemas, calls them, and reads back results. Anthropic published the open spec in November 2024 (Anthropic MCP announcement) and the reference servers now cover GitHub, Postgres, Slack, and 40+ other systems.
A2A is a peer-to-peer contract between two agents that don’t share memory. Google introduced it in April 2025 with 50+ launch partners (Google A2A announcement) and donated it to the Linux Foundation two months later (Linux Foundation A2A project). A2A assumes each agent is a black box with its own model, memory, and tools; they negotiate capabilities through “Agent Cards” and exchange typed messages over HTTP + Server-Sent Events.
Put differently: MCP widens one agent’s reach into deterministic systems. A2A lets non-deterministic agents talk to each other without leaking internals.
When each one wins
# Routing rule we use in production
decision:
- need: "give the agent access to a database/API/file"
pick: MCP
reason: "deterministic tool call, structured I/O"
- need: "a specialist agent (owned by another team/vendor) must do a sub-task"
pick: A2A
reason: "opaque reasoning, async long-running task, no shared stack"
- need: "both"
pick: both
reason: "A2A for the handoff, MCP inside each agent for its tools"The orchestrator doesn’t know how the tax agent reasons about VAT — it only knows the Agent Card promised a file_vat_return skill with a given input schema. That boundary is the point. Inside the tax agent, MCP handles the deterministic calls into ANAF’s e-Factura system under OPANAF 1.581/2025.
Why the confusion is expensive
Teams that pick one and force-fit the other end up re-inventing the missing half. MCP-only shops write brittle “tool wrappers” around other teams’ agents, losing their reasoning capability and streaming updates. A2A-only shops wrap every database behind a fake agent persona, paying LLM latency for what should be a SQL call. Both failure modes show up in the agent failure taxonomy we track internally and align with NIST AI 600-1 guidance on component boundary clarity.
The practical rule we apply on client engagements in our agentic AI work: if the sub-task has a deterministic contract and lives in your stack, it’s an MCP tool. If it has its own model, its own context window, or belongs to another team, it’s an A2A peer. Mixing them in one agent is normal — picking between them is the error.
For teams designing this layer now, our orchestrator decision framework covers when adding an orchestrator actually pays back, and the ENISA multi-agent risk notes cover the attack surface each boundary introduces.