MCP vs A2A vs ACP: Open Protocols for Multi-Agent Systems, Compared

A developer published a comparison of open protocols for multi-agent systems, contrasting Anthropic's MCP for agent-to-tool connections, Google's A2A for agent-to-agent delegation, and alternatives including ACP, ANP, and AG-UI. The writeup maps each protocol to a distinct layer — tool/data, agent, and user/UI — and includes a working Python MCP server example using the official SDK, arguing that open standards turn N×M integrations into N+M.

If you're building a multi-agent system, you've hit this question: how should agents talk to tools, and how should they talk to each other? Wire everything by hand and every new agent adds another custom integration. Pick the wrong protocol and you may rewrite your architecture in six months. This article compares the main open protocols for multi-agent systems: MCP, A2A, ACP, ANP, and AG-UI . It's an MCP vs A2A comparison first, but it also covers the alternatives, because no single protocol solves every layer. By the end you'll know what each protocol does, where they overlap, where they don't, and which combination fits your stack. You don't need to have built an agent system, but this will be easier if you have: pip install "mcp cli " Note: Agent protocols are evolving quickly. Spec details below are accurate as of writing, but always check each protocol's official repository for the current version before you build. A single agent with a few tools works fine with hardcoded glue code. Multi-agent systems break that approach: Open protocols turn N×M into N+M. Each agent or tool implements the standard once and interoperates with everything else that does. Most confusion in the MCP vs A2A debate comes from treating them as competitors. They mostly solve different layers : | Layer | Question it answers | Main protocols | |---|---|---| | Agent ↔ Tool/Data | "How does my agent call a database, API, or file system?" | MCP | | Agent ↔ Agent | "How does my agent delegate work to another agent?" | A2A, ACP, ANP | | Agent ↔ User/UI | "How does my agent stream state to a frontend?" | AG-UI | Keep this table in mind. It resolves most "which one should I use?" questions. Created by: Anthropic announced November 2024 , now an open standard with broad industry adoption and Linux Foundation-backed governance. What it does: MCP standardizes how an LLM application connects to external tools, data sources, and prompts. An MCP client inside your agent or host app talks to MCP servers that expose capabilities. Core primitives: Wire format: JSON-RPC 2.0, over stdio local or Streamable HTTP remote . Here's a small server exposing one tool, using the official Python SDK: python inventory server.py from mcp.server.fastmcp import FastMCP mcp = FastMCP "inventory" STOCK = {"SKU-001": 42, "SKU-002": 0} @mcp.tool def check stock sku: str - dict: """Return the current stock level for a SKU.""" qty = STOCK.get sku if qty is None: return {"sku": sku, "error": "unknown SKU"} return {"sku": sku, "in stock": qty 0, "quantity": qty} if name == " main ": mcp.run transport="stdio" Any MCP-compatible client can now discover and call check stock , with no custom integration code. Strengths Limitations stdio servers need careful security review, since you're running third-party code Created by: Google announced April 2025 , later donated to the Linux Foundation, with many industry partners contributing. What it does: A2A lets independent agents, possibly built on different frameworks by different vendors, discover each other, delegate tasks, and exchange results without sharing internal state, memory, or tools. Core concepts: Wire format: JSON-RPC 2.0 over HTTP S , with Server-Sent Events for streaming recent versions also add gRPC support . { "name": "Refund Agent", "description": "Evaluates and processes customer refund requests.", "url": "https://agents.example.com/refunds", "version": "1.0.0", "capabilities": { "streaming": true }, "defaultInputModes": "text/plain" , "defaultOutputModes": "application/json" , "skills": { "id": "evaluate-refund", "name": "Evaluate refund", "description": "Checks order history and policy to decide refund eligibility.", "tags": "support", "payments" } } Another agent fetches this card, learns what the Refund Agent can do, and sends it a task. It never needs to know what model, framework, or tools sit behind it. Tip: The key design idea in A2A is that agents are opaque . They collaborate through declared capabilities, not by sharing internals. That's what makes cross-vendor collaboration realistic. An honest comparison includes the options that aren't the headline names. Originated by IBM Research and the BeeAI project, ACP took a REST-first approach to agent interoperability, aiming for simple HTTP semantics without requiring a specialized SDK. In 2025 the ACP team announced it was merging into A2A under the Linux Foundation. If you're starting fresh, treat A2A as the successor. If you have existing ACP agents, check the migration guidance from the BeeAI project. ANP targets an open, decentralized "internet of agents" , using decentralized identifiers DIDs for identity and trust between agents that don't belong to the same organization. It's the most ambitious option for cross-organization discovery, and also the least mature in terms of production adoption. AG-UI isn't an agent-to-agent protocol. It standardizes how an agent streams events to a frontend messages, tool calls, state updates . It's complementary: you might use MCP for tools, A2A for agent delegation, and AG-UI to render it all in a web app. Other options include the earlier Agent Protocol a simple REST API for running agent tasks and framework-specific messaging inside tools like LangGraph, CrewAI, or AutoGen. These work well inside a single framework. They're not interoperability standards, so you'll still need an open protocol at your system's boundary. | | MCP | A2A | ACP | ANP | AG-UI | |---|---|---|---|---|---| | Primary layer | Agent ↔ tool/data | Agent ↔ agent | Agent ↔ agent | Agent ↔ agent decentralized | Agent ↔ user/UI | | Origin | Anthropic | Google → Linux Foundation | IBM / BeeAI → merging into A2A | Open community | CopilotKit / open source | | Transport | stdio, Streamable HTTP | HTTP + SSE gRPC in newer versions | REST over HTTP | HTTP + DID-based identity | Event streaming | | Discovery | Client config / registries | Agent Cards | Agent manifests | DID documents | N/A | | Long-running tasks | Limited | First-class | Supported | Varies | Streaming events | | Ecosystem maturity | High | Growing fast | Folding into A2A | Early | Growing | | Best for | Giving agents tools | Cross-team / cross-vendor agent delegation | Legacy BeeAI setups | Open-internet agent networks | Agent-powered frontends | Start with the problem, not the protocol: Rule of thumb: MCP is how an agent uses things. A2A is how an agent works with other agents. In production, these are complementary. A typical layered setup looks like this: User ── AG-UI ──► Orchestrator Agent │ ┌───────────┼────────────┐ A2A A2A MCP ▼ ▼ ▼ Refund Agent Shipping Agent Inventory DB tool │ │ MCP MCP ▼ ▼ Payments API Carrier API The orchestrator delegates to specialist agents over A2A. Each specialist uses MCP to reach its own tools. Neither protocol needs to know about the other, and that separation is why the combination works. Key takeaways: The right setup depends on your architecture, not on which protocol is trending. Start with MCP for tools, add A2A when agents truly need to cross boundaries, and keep each layer swappable. What are you using in your multi-agent stack today: MCP, A2A, something custom? Share your experience in the comments.