MCP vs A2A vs Function Calling: How Your AI Agent Connects to the Rest of Your Software

Every vendor pitching you an AI agent in Dubai this quarter is using at least one of these three protocols. Most will not name them. And the choice quietly decides whether you can swap tools next year without rebuilding the whole thing.

If you are evaluating an agent proposal, the "mcp vs a2a" debate is not what you think it is. These are not two products you pick between.

They are two of three layers in how an agent talks to your software, and Function Calling is the third. Understand the layers and the vendor questions get much sharper.

This article is the buyer's translation of a debate developers have been having for a year. No code. Just the mental model you need before your next AI vendor meeting.

Key Takeaways

• These three protocols sit at different layers — Function Calling is governed by the model vendor's own API, MCP by Anthropic, and A2A by Google Cloud. Most mature agent systems end up using more than one of them together, not just one.
• Each protocol answers to a different vendor — Function Calling definitions are written to one vendor's API format and don't transfer to another model. MCP connectors are model-agnostic and reusable. A2A example: a WhatsApp-facing client agent handing work to a remote scheduling or document-processing agent.
• MCP works today; A2A is still maturing — MCP for structured tool access is the established layer. A2A is emerging, and practitioners are still working through schema drift between organisations, agents over-messaging each other into deadlock, and securing execution without every developer needing to be an IAM wizard.
• Open standards decide what you can change — A vendor built on MCP-compatible integrations keeps you portable across tools and models. One running on proprietary function calls locks you into that vendor's stack, turning a tool swap or model change into a rebuild instead of a config change.
• Ask about standards, not "AI agent" support — Ask which connection standards the integrations follow, whether tool access is built to the MCP standard, whether the coordination layer follows A2A or is proprietary, and to see the WhatsApp and CRM integrations working live rather than on a slide.

Three Layers, Not Three Options

Here is the mistake almost every buyer makes when they see the "mcp vs a2a" comparison. They read it as a fork in the road: pick one, live with it. That framing leads to bad vendor questions.

These protocols solve different problems at different levels of the same stack. Function Calling is the model-native mechanism that predates the other two, the way a language model triggers a structured action.

MCP is how an agent reaches external tools and APIs in a portable way. A2A is how agents coordinate with each other. If any of the vocabulary feels wobbly, our glossary for business buyers walks through the terms one at a time.

Both MCP and A2A are open standard protocols, but they cover different use cases. Function Calling is neither open nor a protocol in the same sense.

Treating the three as interchangeable is like arguing whether you should buy an engine, a road or a steering wheel. The right answer is usually all three, and the interesting question is which brand of each.

Each layer solves a different problem, which is easiest to see side by side.

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Function Calling: The Mechanism Every Agent Already Uses

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Function Calling is built into the model API itself. A developer defines a function, the model decides when to invoke it, and the result flows back into the model's next response. It is the default for a lot of simple agents.

The catch is coupling. Function definitions are written to one model vendor's API format, so they do not travel cleanly to a different model. If your agent has ten function integrations and you decide to swap the base model, that is ten reworks, not a config change.

This is the exact gap MCP was designed to close: it makes tools safe, structured, and reusable across different models or vendors. Function Calling was never built to be that.

For a UAE business owner, Function Calling is fine when the workflow is small, single-model and unlikely to change vendors. The moment you plan to add a second AI vendor or swap the underlying LLM, the coupling stops being an implementation detail and starts being a switching cost.

MCP: How Your Agent Reaches Tools, APIs, and External Data

MCP, the Model Context Protocol, was developed by Anthropic. It is a structured way to let AI agents access tools, APIs, or external resources, and it is an open standard rather than a proprietary interface.

Because MCP is model-agnostic, an integration built to the MCP standard can be reused across different models without rewriting the connector. Build once against MCP, run against Claude today, run against a different model tomorrow, keep the connector.

For a UAE stack, this layer matters most. WhatsApp as your primary customer channel. A CRM handling Arabic-English bilingual conversations in the same thread.

Document handling has to respect Federal Decree-Law No. 45 of 2021, the UAE Personal Data Protection Law. Each of these needs a connector between your agent and the underlying system. The right question to ask a vendor is "is the WhatsApp connector built to the MCP open standard", not "can it talk to WhatsApp".

What MCP does not do is coordinate one agent with another. It gives a single agent a clean, portable way to reach the outside world. For coordination between agents, that is A2A's job.

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A2A: When One Agent Needs to Hand Work to Another

A2A, Agent-to-Agent communication, is a concept being defined by Google Cloud. It describes how agents collaborate to accomplish tasks, sharing goals, dividing work and passing artifacts between them.

In an A2A design, agents fall into two categories. Client agents initiate requests and coordinate tasks on behalf of the user. Remote agents receive those requests and execute.

The client agent might be your customer-facing WhatsApp agent. The remote agent might be a scheduling agent, or a document-processing agent. MCP alone cannot do this.

How much of this you actually need depends on how much autonomy your agents are allowed. Our piece on how much your AI agent should be allowed to decide covers that trade-off in detail.

Now the honest part: A2A is early. Practitioners note that most teams have not yet made the A2A protocol work reliably across vendors, and that frontier models still lack good planning capabilities in domain-specific agents.

Treat that as your calibration when a vendor pitches a fleet of collaborating agents. Ask what is really running versus what is on a slide.

For a UAE buyer evaluating a multi-agent proposal, the question is direct. Does the coordination layer follow the A2A open standard, or is it a proprietary orchestration layer sitting inside one vendor's platform?

MCP vs A2A in Practice: What Teams Are Actually Wiring Today

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In theory, most mature agent systems need both. One layer to fetch tools and data, which is MCP's job. Another to coordinate agent behaviour, which is A2A's.

Here is the real state of play. MCP for structured tool access is the established layer. A2A for peer-to-peer coordination is emerging, and practitioners are candid that it needs planning capabilities current frontier models do not reliably have.

Practitioners name three open problems that anyone wiring these systems is still solving. Schema drift across organisational boundaries, when two agents disagree on what a "customer record" looks like. Over-messaging, where agents chatter until they deadlock instead of finishing the task.

The third is securing execution without needing every dev to be an IAM wizard. These are not solved. They are what real teams are working on right now.

This is also where vendor marketing lives. When a demo shows an "AI employee" handling a full workflow end to end, the coordination underneath is usually simpler than the pitch suggests. Our piece on what "AI employee" actually means breaks that down.

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What These Protocols Mean When Evaluating an AI Agent System in the UAE

Here is the translation. The buyer question is not which protocol is better; it is whether your vendor uses open-standard connections or proprietary ones, and what that means the day you need to change a tool, swap a model or add a second agent.

A vendor building on MCP-compatible integrations gives you portability. A vendor whose agent runs on proprietary function calls locks you into their stack. Ask the question early enough that you can price switching cost into the contract.

For the broader frame on what agentic AI actually is, our agentic AI explainer for UAE business leaders is the right starting point.

The UAE-specific stack adds three constraints most global comparisons miss. WhatsApp is your primary customer channel, not email. Workflows carry Arabic and English in the same message thread, sometimes with Arabizi mixed in.

Data handling sits under Federal Decree-Law No. 45 of 2021 and the layered DIFC and ADGM regimes on top of it. Your agent's connection layer has to accommodate all three without a custom rewrite every time one tool updates.

The vendor conversation gets simpler once you know the layers. Not "do you do AI agents". Instead: which standards do your integrations follow, is your tool access built to MCP, and what changes on my side if I add a tool or switch the model.

If you want a second pair of eyes on an agentic proposal, book a free 30-minute consultation with Lenoo AI. We will read the vendor's architecture with you and tell you honestly which layers use open standards and which do not.

FAQ

What is the difference between MCP, A2A, and Function Calling in plain language?

Function Calling is how a single language model triggers a specific action, built into the model's own API. MCP is an open standard from Anthropic for how an agent reaches external tools and data in a way that works across different models. A2A is an emerging standard from Google Cloud for how one agent hands work to another.

Do I need to choose between MCP and A2A, or can an AI agent use both?

You do not choose between them, and a mature system usually uses both. MCP handles tool access. A2A handles coordination between agents.

Is A2A ready for production today?

It is early. Most agentic implementations have not yet made the A2A protocol work reliably across vendors, and frontier models still lack the planning capabilities needed for complex multi-agent workflows. Treat vendor claims of production A2A with scrutiny.

If my AI vendor uses proprietary connections instead of MCP, should I worry about lock-in?

Concerned rather than panicked. Proprietary connections work today, but the exposure is what happens when you want to add a new tool, swap the model or bring in a second AI vendor. Each of those turns into a rebuild rather than a config change.

What questions should I ask an AI vendor about how their agent connects to my software?

Four direct ones. Which connection standards do your integrations follow. Is your tool access built to the MCP open standard.

Is your coordination layer aligned with A2A or a proprietary orchestration. And can you show me the WhatsApp and CRM integrations in a working demo, not just a slide?

Can a well-built MCP implementation enable some agent-to-agent coordination without needing A2A?

Some practitioners say a well-implemented MCP layer can support certain forms of agent-to-agent interaction, because tool access and coordination sometimes overlap. That does not make MCP a replacement for A2A. The boundary is fuzzier than a clean diagram suggests.

Does the connection protocol affect how my agent handles Arabic and English tools in the UAE?

Indirectly, yes. The protocol itself is language-agnostic, but portability decides whether you can plug in a better bilingual CRM, a WhatsApp Business API upgrade or a PDPL-aligned document tool without a rewrite. In a market where WhatsApp is the primary channel and Arabic-English workflows are the norm, that flexibility compounds fast.