MCP vs A2A: what each protocol lets you inspect
MCP and A2A operate at different layers of an agent system, so the comparison is less “which one wins” and more “which layer are you building”. The Model Context Protocol connects an agent to its tools and context: databases, APIs, file systems, exposed as typed functions the agent can call. The Agent2Agent protocol connects an agent to other agents: independent peers with their own models, their own tools and their own goals, which collaborate without showing you any of that. Most real multi-agent systems end up using both. The distinction worth actually understanding, and the one most A2A vs MCP comparisons skip, is what each protocol lets the caller see, because that determines what kind of trust decision you are making when you wire one in.
Both protocols are now vendor-neutral open standards. MCP was created by Anthropic in November 2024 and was contributed to the Linux Foundation’s Agentic AI Foundation in December 2025; its current revision is 2026-07-28. A2A was announced by Google in April 2025 and transferred to the Linux Foundation in June 2025; its current specification is version 1.0, its first stable release.
MCP vs A2A at a glance
| MCP | A2A | |
|---|---|---|
| Connects | An agent to tools and data | An agent to other agents |
| Unit of interaction | A tool call with typed arguments | A task with a lifecycle |
| What the remote side exposes | Its full call surface: every tool’s name, description and JSON Schema | An Agent Card describing skills in natural language; internals hidden by design |
| Discovery | tools/list over the connection | Agent Card at /.well-known/agent-card.json, or a registry |
| Wire format | JSON-RPC 2.0 over stdio or Streamable HTTP | JSON-RPC 2.0, gRPC or HTTP/REST bindings |
| State model | Stateless since 2026-07-28; state via explicit handles | Stateful tasks: submitted, working, input-required, completed, failed |
| Current spec | Revision 2026-07-28 | Version 1.0 |
| Governance | Agentic AI Foundation (Linux Foundation), contributed by Anthropic | Linux Foundation, contributed by Google |
| Question it answers | ”What can my agent do?" | "Who can my agent delegate to?” |
What is the actual difference between A2A and MCP?
MCP is a tool protocol. A client sends a tools/list request and the server must return the set of tools available to that caller: a name, a description, an inputSchema that is a full JSON Schema for the arguments, and optionally an outputSchema for the result. The list is paginated, and the spec allows it to vary with the authorisation the caller presents, so what you enumerate is the surface offered to your credentials rather than a universal one. The agent then invokes a specific tool with specific typed arguments via tools/call and gets a result back. Within that scope the call surface is declared up front: every parameter of every operation is visible before the first call is made.
A2A is a delegation protocol. A client reads a remote agent’s Agent Card, a JSON document served at /.well-known/agent-card.json (RFC 8615 well-known URI) that describes the agent’s identity, service endpoint, authentication schemes and a list of skills. A skill is not a call signature. It has an id, a name, a natural-language description, input and output modes, and examples. To use it, you do not call a function; you send a message describing what you want, the remote agent opens a task, and that task moves through lifecycle states (submitted, working, input-required, completed, failed and others) until it produces artifacts. How the remote agent gets from your message to those artifacts, which model it runs, which tools it calls, how it plans, is deliberately not part of the protocol.
That opacity is not an accident of a young spec. It is A2A’s stated design principle. The specification describes agents collaborating “without needing access to each other’s internal state, memory, or tools”, and the project README is even more direct about why: agents “collaborate without needing to share internal memory, proprietary logic, or specific tool implementations, enhancing security and protecting intellectual property”.
So the clean one-line version of MCP vs A2A: MCP exposes a call signature; A2A advertises a capability. An MCP server tells you exactly what it will do if invoked. An A2A agent tells you roughly what it is good at, and asks you to hand the work over.
What does this mean for trust?
This is the part of the agent to agent protocol vs MCP comparison that gets skipped, and it is the part we care about most, because inspecting declared surfaces is what VerifyMCP does all day.
An MCP server’s tool surface is inspectable before you trust it. Because every tool arrives as a schema, you can read it, diff it between scans, and score it without ever executing a tool. That is precisely how our scoring engine works: we connect to every server in the official MCP registry, 23,066 servers as of 18 August 2026, read their declared tool schemas, and check their transport, TLS and authentication posture. A declared surface also makes drift detectable: if a server quietly swaps a benign tool description for one that instructs the model to exfiltrate data (a rug pull), the change is visible in the schema diff. What a schema cannot tell you is what the implementation does with the arguments once they arrive, so treat it as the declared surface and a claim to check, not as proof of behaviour. The MCP spec itself is explicit that this surface is where scrutiny belongs: tool descriptions and annotations must be treated as untrusted unless they come from a trusted server.
An A2A agent gives you far less to inspect, on purpose. You can verify the things the protocol does declare: that the Agent Card is served over TLS, which authentication schemes it demands, that the endpoint honours the task lifecycle. What you cannot do, even in principle, is audit the remote agent’s reasoning, its system prompt, its model or the MCP servers it calls on your behalf, because the protocol is designed so that none of that crosses the wire. The skill descriptions in an Agent Card are self-declared natural language with no schema to validate behaviour against and no way to diff intent.
Neither model is wrong. They are different trust decisions:
- Adopting an MCP server is like vendoring a library. The surface is declared, so review, pinning and continuous scoring are possible, and you should use them. Our threat model for MCP covers what can still go wrong inside that declared surface.
- Delegating over A2A is like hiring a subcontractor. You cannot review their working, so trust rests on identity (who operates this agent), authentication, scoping (what credentials and data the task carries), and track record. The card tells you what they claim; only their outputs tell you what they do.
Authentication posture already looks different at the two layers today. A2A version 1.0 builds authentication requirements into the Agent Card itself, so a client knows before the first request which scheme the agent demands. MCP’s authorisation spec is newer and declared adoption is still uneven: as of 18 August 2026, 1,487 of 12,682 remote components in the official MCP registry declared a required secret header in their entry, a floor rather than a count of enforcement. If you maintain one of the rest, our remote authentication guide walks through fixing that.
Do you need both MCP and A2A?
Usually, yes, if you are building anything with more than one agent in it. The protocols compose vertically and horizontally: A2A between agents, MCP from each agent down to its own tools. The A2A project describes itself as complementing MCP for exactly this reason.
Concretely: a procurement agent receives “renew our SaaS contracts under budget”. It delegates the pricing research to a vendor-analysis agent over A2A, as a task, because that agent belongs to another team and its internals are not the procurement agent’s business. The vendor-analysis agent, internally, calls its own MCP servers (a contracts database, a spend API) as typed tool calls. Each protocol carries the interaction it was designed for: opaque delegation across a trust boundary, transparent tool calls inside one.
The overlap between them is small but real, and worth naming precisely. MCP’s 2026-07-28 revision moved tasks out of the core protocol into an official Tasks extension, which handles asynchronous, long-running operations by polling tasks/get for durable handles rather than blocking on a result. So “the work takes a long time” is no longer, by itself, a reason to reach for A2A. The reason to reach for A2A is that the thing on the other end is a peer that reasons, operated by someone else, whose internals you are not entitled to see. If the thing on the other end is a capability you own or fully trust the definition of, model it as an MCP tool and keep the surface inspectable.
When should you use MCP instead of A2A?
- Use MCP when your agent needs data or actions: files, databases, APIs, browsers. You want the typed schema, the reviewability and the human-in-the-loop consent flow the spec expects.
- Use A2A when the counterpart is another reasoning system, especially one operated by a different team or company, and the interaction is “achieve this outcome” rather than “execute this operation”.
- Do not wrap an agent as an MCP tool just to avoid A2A if it crosses an organisational boundary. The schema you would publish for it (“input: string, output: string”) declares nothing, which gives you MCP’s obligations with A2A’s opacity and neither protocol’s benefits.
The layer you can inspect is the layer you can score. That is why VerifyMCP scores MCP servers and not A2A agents: a declared tool surface supports independent, repeatable measurement, and an opaque peer does not. If your agents sit on top of MCP servers, start with what is measurable today: browse the registry to see how the servers you depend on score, and read how the scoring works to see exactly which declared signals feed it. Those scores are independent estimates rather than guarantees, and anything we cannot verify is marked inconclusive rather than guessed.