MCP vs A2A — The Agent Protocol Landscape in June 2026

The agent protocol landscape in June 2026 has consolidated around two standards: Model Context Protocol (MCP), originated by Anthropic in late 2024, and Agent-to-Agent Protocol (A2A), introduced by Google in April 2025. There's also ACP (Agent Communication Protocol) from the OpenAgent project, and a handful of payment-rail proposals layered on top. But MCP and A2A are the two protocols you'll actually encounter in production systems.

The shorthand that stuck — from a Register piece in July 2025 — is useful: MCP is the USB-C of AI agents. A2A is their Ethernet. One connects tools to a single agent. The other connects agents to each other.

What MCP actually does

MCP is a client-server protocol. An agent (the client, or "host" in MCP terminology) connects to one or more MCP servers. Each server exposes a set of tools — functions the agent can call. The protocol defines three primitives:

1. Resources — Data the agent can read (files, database records, API responses)
2. Tools — Functions the agent can invoke (run a SQL query, send an email, create a Jira ticket)
3. Prompts — Templated interactions the server provides (pre-written system prompts for specific tasks)

MCP uses JSON-RPC 2.0 over stdio or HTTP (with SSE for streaming). The transport is deliberately simple — stdio for local tools, HTTP for remote ones. A single agent maintains multiple concurrent MCP connections, one per tool server.

What MCP does not do: agent-to-agent communication. An MCP server is a tool provider, not a peer agent. Two agents don't talk MCP to each other. This is the gap A2A fills.

What A2A actually does

A2A is a peer-to-peer protocol for agent collaboration. It defines:

1. Agent Cards — A JSON manifest describing an agent's capabilities, endpoints, and authentication requirements. Think of it as an OpenAPI spec for an agent.
2. Tasks — The unit of work. An agent sends a task to another agent, which can be synchronous (single response) or asynchronous (the receiving agent works on it and returns results later).
3. Artifacts — Structured outputs from completed tasks, with versioning and provenance tracking.

A2A uses HTTP + JSON by default, with optional gRPC for high-throughput agent clusters. It assumes agents are long-lived services with their own identity, not ephemeral tool wrappers.

The key insight: A2A doesn't care what model or framework the agent uses. An agent built with LangGraph can delegate to an agent built with CrewAI, which can call back to a Claude-powered agent. The protocol is the interoperability layer.

Where they overlap (and where people get confused)

Both protocols serialize structured data. Both use JSON. Both aim to make agents interoperate with something external. And both are sometimes described as "the standard for AI agents." That's where the confusion starts.

The overlap is thin. MCP is about giving a single agent access to tools — your agent can query PostgreSQL, search your email, and create a Linear ticket because three MCP servers expose those capabilities. A2A is about multiple agents collaborating — your coding agent delegates a database migration to your DBA agent, which reports back with a verified plan.

What about ACP?

ACP (Agent Communication Protocol), proposed by the OpenAgent project in early 2025, aims to unify both layers — tool access and agent communication — under a single specification. It's more ambitious and less adopted. ACP defines agents, tools, and tasks in a unified model, with a richer capability negotiation handshake.

The trade-off: ACP is more complete but heavier. MCP + A2A together cover 90% of use cases with simpler implementations. ACP has early adopters in academic research and a few enterprise deployments, but hasn't reached the critical mass of either MCP or A2A individually.

Our read: ACP is worth watching but not worth building against yet. The MCP+A2A combination has more tooling, more documentation, and more production deployments you can learn from.

Which one should you build against?

The answer depends on what you're building:

If you're building a single agent that needs tools — use MCP. It's the de facto standard. Every major coding agent (Claude Code, Cursor, Copilot, Codex) supports MCP servers. Building your internal tools as MCP servers means they're immediately usable by any agent your team adopts.

If you're building a multi-agent system — plan for A2A between agents and MCP for each agent's tools. Don't invent your own inter-agent protocol. The A2A spec is mature enough for production (v1.1 as of May 2026), and the ecosystem of A2A-compatible agent frameworks is growing.

If you're building for Indian enterprise — lead with MCP. Indian SMBs adopting AI agents are overwhelmingly at the single-agent-with-tools stage. A GST filing agent that uses MCP to connect to Tally and the GST portal is more immediately valuable than a multi-agent orchestration that most Indian mid-market companies don't have the infrastructure to run.

The real bottleneck isn't the protocol

Protocol discussions are useful but miss the point. The hard problem in agent systems isn't how agents communicate — it's what they communicate about. Shared context, shared memory, shared understanding of domain concepts.

An agent that can call your PostgreSQL database through MCP is useful. Two agents that can delegate tasks through A2A is useful. But neither protocol solves the problem of making sure both agents agree on what "a customer" means, or that "order status = pending" means the same thing to your inventory agent and your notification agent.

This is where we spend most of our time at Krypton Forge: not picking protocols, but designing the domain models that sit underneath them. The protocol is plumbing. The domain model is architecture.

What to do tomorrow

If you haven't built any MCP servers yet, start with one. Pick an internal tool your team uses daily — Slack, Linear, your database — and wrap it in an MCP server. The @modelcontextprotocol/sdk (TypeScript) and mcp (Python) packages make this a 30-minute exercise for a simple read-only tool.

If you're already using MCP and wondering about A2A, wait until you have a concrete multi-agent use case. The protocol isn't the bottleneck — the use case is. When you have two agents that genuinely need to collaborate (not just call each other's tools), A2A is ready. Before that point, it's premature abstraction.