MCP vs A2A: What's the Difference and When to Use Each

MCP connects agents to tools. A2A connects agents to each other. They solve different problems, live at different layers, and you will likely use both.

That one-sentence distinction eliminates 90% of the confusion in online discussions about these protocols. The remaining 10% is worth understanding, because the architecture decisions you make now — which protocol handles what — will determine whether your multi-agent system scales or collapses into a tangle of custom integrations.

Both protocols are now governed by the Agentic AI Foundation (AAIF) under the Linux Foundation, with 146 member organizations including Anthropic, Google, OpenAI, Microsoft, AWS, and Bloomberg. This is not a standards war. It is a coordinated division of labor.

What MCP Does

The Model Context Protocol (MCP) is the standard interface between an AI agent and external tools, data sources, and services. Anthropic released it in November 2024. As of March 2026, MCP has 97 million monthly SDK downloads across the TypeScript and Python packages, with thousands of servers on public registries.

MCP answers one question: how does an AI agent call a tool?

Concrete example

You install the Toolradar MCP server in Claude Desktop. You ask Claude: "What does Figma cost?"

Claude sees it has access to a get_pricing tool. It reads the tool description, decides this tool can answer the question, constructs the parameters { slug: "figma" }, makes the call, and tells you: "Figma has a free Starter plan, Professional at $15/editor/month, Organization at $45/editor/month, and a custom Enterprise tier."

You wrote zero code. Claude discovered the tool, decided to use it, and handled the response. That is MCP.

What MCP provides

• Tool discovery — an MCP server advertises what it can do (list tools, read data, execute actions)
• Structured invocation — the agent calls tools with typed parameters and gets structured responses
• Cross-platform compatibility — the same MCP server works with Claude, Cursor, VS Code Copilot, ChatGPT, Gemini, and any compliant client
• Local and remote deployment — servers run on your machine (stdio) or over the network (HTTP with SSE)

MCP replaced the fragmented world where every AI tool integration was a bespoke plugin. It is the USB-C port for AI agents — one connector, thousands of peripherals.

What A2A Does

The Agent2Agent Protocol (A2A) is the standard for AI agents communicating with other AI agents. Google released it in April 2025. It reached v1.0 in early 2026 with support for gRPC, signed Agent Cards, and multi-tenancy.

A2A answers a different question: how does an AI agent delegate work to another AI agent?

Concrete example

You have a travel-planning agent. A user asks: "Book me a flight to Tokyo and find a hotel near Shibuya for under $200/night."

Your agent cannot do both tasks alone. It uses A2A to discover a flight-booking agent (via its Agent Card at /.well-known/agent-card.json), sends the flight request, then discovers a hotel-booking agent and sends the accommodation request. The agents negotiate asynchronously — the hotel agent might ask for date flexibility, the flight agent might stream status updates. When both tasks complete, your planner agent assembles the final itinerary.

Each of those specialist agents, internally, uses MCP to call their own tools: airline APIs, hotel databases, payment processors. But the communication between agents — the discovery, delegation, negotiation, and status tracking — is A2A.

What A2A provides

• Agent discovery — agents publish Agent Cards describing their capabilities, authentication requirements, and supported protocols
• Task delegation — a client agent sends a task to a remote agent, which can accept, reject, or negotiate
• Multi-turn dialogue — agents exchange messages, artifacts, and status updates over the life of a task
• Opaque execution — the calling agent does not need to know what framework, model, or tools the remote agent uses
• Enterprise security — signed Agent Cards with cryptographic identity verification, TLS-mandatory gRPC transport

A2A treats each agent as a black box with a well-defined interface. This is critical in enterprise environments where different teams build agents on different stacks — one on LangChain, another on CrewAI, a third on a proprietary framework. A2A lets them collaborate without sharing internals.

Side-by-Side Comparison

When to Use MCP

Use MCP when your agent needs to interact with tools, databases, APIs, or file systems.

• Single-agent applications — a coding assistant that reads your codebase, runs tests, and queries documentation
• Tool-heavy workflows — an agent that searches the web, queries a database, and writes to a spreadsheet
• Replacing custom integrations — instead of building bespoke plugins for each AI platform, build one MCP server that works everywhere
• Any scenario where the "other side" is not intelligent — if it is a database, an API, or a service, MCP is the right protocol

If you are building your first AI tool integration, start with MCP. The ecosystem is mature, the tooling is solid, and every major AI platform supports it.

When to Use A2A

Use A2A when your agent needs to collaborate with other agents — especially agents you did not build.

• Multi-agent orchestration — a planning agent that delegates subtasks to specialist agents
• Cross-organization collaboration — your company's agent needs to interact with a vendor's agent
• Long-running tasks — jobs that take minutes or hours, requiring status updates and intermediate results
• Heterogeneous environments — agents built on different frameworks, running on different infrastructure
• Any scenario where the "other side" is intelligent — if it can reason, negotiate, or ask clarifying questions, use A2A

When to Use Both

Most production multi-agent systems will use both protocols at different layers. This is the intended design.

Architecture: How MCP and A2A stack together

┌─────────────────────────────────────────────┐
│                   User                       │
│              "Plan my trip"                   │
└──────────────────┬──────────────────────────┘
                   │
                   ▼
┌─────────────────────────────────────────────┐
│           Planner Agent                      │
│                                              │
│  Uses A2A to delegate ──►  Flight Agent      │
│  Uses A2A to delegate ──►  Hotel Agent       │
│  Uses A2A to delegate ──►  Budget Agent      │
└─────────────────────────────────────────────┘
                                    │
         ┌──────────────────────────┼──────────────────┐
         ▼                         ▼                    ▼
┌────────────────┐    ┌────────────────┐    ┌────────────────┐
│  Flight Agent  │    │  Hotel Agent   │    │  Budget Agent  │
│                │    │                │    │                │
│ Uses MCP for:  │    │ Uses MCP for:  │    │ Uses MCP for:  │
│ • Airline API  │    │ • Booking DB   │    │ • Spreadsheet  │
│ • Calendar     │    │ • Reviews API  │    │ • Exchange API │
│ • Payment      │    │ • Maps         │    │ • Calculator   │
└────────────────┘    └────────────────┘    └────────────────┘

The planner agent speaks A2A outward (to other agents) and MCP downward (to its own tools). Each specialist agent does the same. A2A handles the delegation and coordination. MCP handles the tool calls.

The A2A documentation uses a fitting analogy: think of an auto repair shop. The shop manager (planner agent) talks to the customer and coordinates with the mechanic and parts supplier (A2A). The mechanic uses diagnostic scanners, repair manuals, and the platform lift (MCP). The tools do not negotiate. The agents do.

What About Function Calling?

If you have used the OpenAI API, you know function calling — you define tools in your API request, and the model returns structured calls to those functions. How does this relate to MCP?

Function calling is per-request, vendor-specific tool definition. You paste your tool schemas into every API call. It works, but it means:

• Every AI platform has its own function-calling format
• Tool definitions live in your application code, not in a discoverable server
• There is no standard way for an agent to discover new tools at runtime

MCP is the cross-vendor, persistent alternative. Instead of pasting tool schemas into API calls, you point your agent at an MCP server. The server advertises its tools. The agent discovers them automatically. The same server works across Claude, ChatGPT, Gemini, Cursor, and any other MCP-compliant client.

Function calling is not going away — it is the low-level mechanism that MCP clients use internally to present tools to models. But if you are choosing an architecture, MCP gives you portability and discoverability that function calling alone cannot.

The AAIF: Why Governance Matters

In December 2025, Anthropic, Block (Square), and OpenAI founded the Agentic AI Foundation under the Linux Foundation. Google contributed A2A. Anthropic contributed MCP. Block contributed goose (an open-source AI agent) and OpenAI contributed AGENTS.md.

As of March 2026, AAIF has 146 members across three tiers:

• Platinum: AWS, Anthropic, Block, Bloomberg, Cloudflare, Google, Microsoft, OpenAI
• Gold: IBM, Salesforce, SAP, Shopify, Snowflake, Docker, JetBrains, Oracle, JPMorgan Chase, American Express, and others
• Silver: Zapier, Hugging Face, Uber, Pydantic, WorkOS, and dozens more

Why does this matter for practitioners? Because vendor lock-in is the primary risk in AI infrastructure. When both MCP and A2A are governed by a neutral foundation — not controlled by any single company — you can build on them without worrying that the protocol will be deprecated, paywalled, or forked for competitive advantage.

IBM's Agent Communication Protocol (ACP), which launched in March 2025 for the BeeAI platform, merged into A2A in August 2025. Kate Blair, who led ACP at IBM Research, joined the A2A Technical Steering Committee. This consolidation is a healthy sign — the industry is converging on fewer, better standards rather than fragmenting into competing protocols.

What Is Next

The MCP and A2A ecosystems are both accelerating.

MCP's 2026 roadmap focuses on enterprise readiness: better auth, observability, and horizontal scaling for HTTP transport. Working groups are driving each area, with specs expected throughout the year. The first MCP Dev Summit takes place April 2-3 in New York City, with 95+ sessions from protocol maintainers, security researchers, and production deployers.

A2A just shipped v1.0, its first production-ready release, with gRPC transport, signed Agent Cards for cryptographic identity, and multi-tenancy support. SDKs are available in Python, Go, JavaScript, Java, and .NET. The Technical Steering Committee includes representatives from Google, AWS, Microsoft, IBM, Cisco, Salesforce, SAP, and ServiceNow.

The trajectory is clear: MCP is becoming the universal tool layer, and A2A is becoming the universal agent coordination layer. Building on both is not hedging — it is the architecture the industry is converging on.

Bottom Line

MCP and A2A are not competitors. They are two layers of the same stack. MCP gives your agent hands. A2A gives your agents the ability to work as a team.

If you want to see MCP in action, explore the best MCP servers for 2026 or try the Toolradar MCP server to give your AI agent access to 10,000+ software tools.