MCP vs A2A: Which AI Agent Protocol Does Your Project Need?

This comparison is for developers and architects worldwide who are designing an AI agent or a multi-agent system and keep seeing both acronyms in proposals. As of 3 October 2026, both protocols are open projects under the Linux Foundation. After reading, you will know what each protocol does, where they overlap, and which one your current project needs.

What is MCP (Model Context Protocol)

MCP is an open protocol, introduced by Anthropic in November 2024, that lets an AI application discover and call tools, read resources and use prompt templates exposed by MCP servers, so one integration works across many models and hosts.

The architecture has three roles. The host is the AI application the user works in. The client is the connector inside the host. The server wraps a system such as a database, a CRM or a file store and exposes named tools with typed arguments. The model decides when to call a tool; the server decides what the tool is allowed to do.

In December 2025 Anthropic donated MCP to the Agentic AI Foundation, a fund under the Linux Foundation. The July 2026 specification revision made the protocol stateless, which simplifies running servers behind load balancers. Details are on modelcontextprotocol.io, and our MCP server guide walks through a working example.

What is A2A (Agent2Agent protocol)

A2A is an open protocol, announced by Google in April 2025 and now a Linux Foundation project, that lets independent agents discover each other's skills, exchange tasks and return results without sharing internal code.

A2A treats each agent as an opaque service. The calling agent does not see the other agent's prompts, tools or model. It sees only what the remote agent advertises in its Agent Card and what it returns. The core objects in the 1.0 specification are:

• Agent Card: a JSON document describing the agent's name, skills, endpoints and required authentication, conventionally served from a well-known path on its domain.
• Task: a unit of work with an ID and a lifecycle state such as submitted, working, input required, completed, failed, canceled or rejected.
• Message and Part: the conversation between agents, made of text, file or structured data parts.
• Artifact: the output a task produces, such as a report or a generated file.

The specification defines JSON-RPC 2.0, gRPC and HTTP plus JSON bindings, and supports common authentication schemes including OAuth 2.0, OpenID Connect, API keys and mutual TLS. The specification is published at a2a-protocol.org.

MCP vs A2A: side-by-side comparison

The simplest way to separate them is by what sits on the other end: with MCP it is a tool or data source you control, with A2A it is another agent that plans on its own.

Aspect	MCP	A2A
Connects	Agent or AI app to tools, data and prompts	Agent to agent
Introduced by	Anthropic, November 2024	Google, April 2025
Governance today	Agentic AI Foundation, Linux Foundation	Linux Foundation project
Other side is	Deterministic code you control	An autonomous agent, possibly another company's
Discovery	Server lists its tools, resources and prompts	Agent Card with skills and auth schemes
Unit of work	Tool call with typed arguments	Task with lifecycle states and artifacts
Long-running work	Tasks extension for durable requests	Built in: task states, streaming, push notifications
Transports	stdio for local, Streamable HTTP for remote	JSON-RPC 2.0, gRPC, HTTP plus JSON
Typical example	Agent checks stock in the ERP	Procurement agent asks a supplier's quoting agent for a price

When you need MCP only

You need MCP only when one assistant or agent works for your own team and needs to read or act on your own systems, which describes most business AI projects in their first year.

Examples that fit MCP alone:

• An owner asks a chat assistant for pending dispatches, outstanding payments and today's production in one conversation.
• A support assistant looks up order status and delivery dates from the order database.
• A finance assistant drafts payment reminders from the accounting ledger for a person to approve.

In each case there is one agent, one owner and a known set of tools. Adding A2A here would add moving parts without solving a problem. If you are still deciding between a chatbot and an agent, read AI agent vs chatbot first.

When you need A2A

You need A2A when separate agents, built or run by different teams, vendors or companies, must find each other and cooperate on tasks that may take minutes or days, without exposing their internals to one another.

• Cross-company workflows. A buyer's procurement agent requests quotes from suppliers' quoting agents, each of which checks its own capacity and pricing privately.
• Large organisations with many teams. An HR agent, a finance agent and an IT agent each owned by a different department, with an orchestrator routing employee requests.
• Agent marketplaces or partner integrations. You publish an agent that other businesses' agents can call, much like publishing an API today.

A2A also handles the slow parts of real business work better than a simple tool call. A supplier's agent may need a person to check a drawing, or a finance agent may wait for a bank confirmation. The task stays open in a known state, the caller can poll or receive a push notification, and the result arrives as an artifact when ready. Building that yourself on top of tool calls is possible but means reinventing the same lifecycle.

The key signal is a trust boundary. If you control both agents and they run in the same codebase, a function call or an internal queue is usually simpler. A2A earns its place when the other agent is a black box you call over the network. Our multi-agent systems guide covers orchestration patterns in more depth.

How MCP and A2A work together in one architecture

In a layered design each agent uses MCP to reach its own tools and data, while A2A carries requests between agents, so the two protocols sit at different layers and rarely compete for the same job.

Consider an export manufacturer that receives enquiries from overseas buyers:

1. The buyer's sourcing agent fetches the manufacturer's Agent Card and sends an A2A task: price and lead time for 5,000 brass fittings to a drawing.
2. The manufacturer's quoting agent accepts the task and uses MCP tools to read raw material rates, machine capacity and past quotes from its ERP.
3. It pauses in the input required state while a sales engineer approves the margin in an internal app.
4. It completes the task and returns the quote as an A2A artifact.

Notice what each protocol carries. The A2A messages contain only business-level content: the request, a status, the quote. The MCP calls contain internal detail: rate tables, machine loading, customer history. Keeping those layers separate is what lets a manufacturer expose an agent to the outside world without exposing its ERP.

The buyer never sees the ERP. The manufacturer never sees the buyer's internal logic. Each side keeps its own security model, and each protocol does one job.

What we see on real agent projects

On agent projects JK Tech Hub has delivered for clients in India, the UK and the Gulf, MCP or plain function calling covered every first release; A2A came up only where agents crossed a company or department boundary.

• A Rajkot pump manufacturer wanted an assistant for dispatch and stock questions. Five MCP tools over the ERP covered it. No second agent was needed.
• A UK distribution client already ran a warehouse agent built by another vendor. The cleanest way to connect our order agent to it was an agent-to-agent interface, so neither side exposed internals.
• A multi-branch hospital group considered separate agents for billing, appointments and pharmacy. We built one agent with grouped MCP tools first, because the teams shared one database and one owner.

Our rule of thumb: count the trust boundaries, not the features. One boundary means MCP. Two or more agents across boundaries means A2A on top.

How to decide between MCP and A2A

Decide by asking what sits on the other end of each connection, who owns it and whether it plans on its own; the table below turns that into a quick decision for most projects.

Your situation	Recommendation
One assistant over your own ERP, CRM or database	MCP only
Several agents in one codebase, one team	MCP for tools, internal function calls between agents
Agents owned by different departments or vendors	MCP inside each agent, A2A between them
Your agent must be callable by customers' or partners' agents	Publish an A2A Agent Card; keep MCP internal
You only need a model to call two or three functions	Plain function calling may be enough today; MCP when tools grow

A practical sequence for most teams: ship one agent with MCP tools, measure it for a quarter, and add A2A only when a second agent with a different owner appears. Both protocols are open, so starting with MCP does not lock you out of A2A later; the internal tools you build stay useful when you add an agent-to-agent layer on top.

Whatever you choose, security rules carry over: least-privilege tools, human approval for payments and irreversible writes, and full logging. See the AI agent security checklist.

Related reading and next step

• AI agent development services
• How to build an MCP server for business data
• Multi-agent AI systems guide
• AI agent development cost in 2026

To get a fixed quote for an agent architecture using MCP, A2A or both, send your use case through the contact page or WhatsApp us at +91 7265004040.