MCP vs A2A Protocol: What's the Difference?

Search for either protocol and you will find them set up as rivals, MCP against A2A, as if you had to back one. You do not. They are not competing for the same job. They work on different axes of the same system, and a production agent stack will usually run both.

The mix-up is easy to make. Both arrived within a year of each other, both put "agent" in the headline, and both describe how an AI system reaches something outside itself. The difference is what they connect and in which direction. The Model Context Protocol (MCP) connects one agent downward, to the tools and data it works with. The Agent2Agent (A2A) protocol connects agents sideways, to each other. One is vertical, the other horizontal.

That distinction decides how you build, and it decides where a data platform like Dawiso fits. MCP is the protocol that carries governed context to an agent, so it is the one that touches data governance directly. A2A sits a layer above, coordinating agents that have already been handed their context. Keep the two axes apart and the useful questions come into focus, like when to reach for each and what neither one handles on its own.

Anthropic introduced the Model Context Protocol in November 2024 to solve a connection problem. An AI model on its own is isolated. It cannot read your database, call your internal API, or open a document unless someone wires that capability in by hand, and every model and every tool needs its own bespoke wiring. MCP standardizes that wiring. It gives any model one consistent way to discover and use external tools, data sources, and prompts, so the integration is written once and works everywhere.

The shape of MCP is a client talking to a server. Your agent runs an MCP client, a tool or data source exposes an MCP server, and they exchange messages over a defined protocol built on JSON-RPC. The server advertises what it offers and the model calls it. This is the vertical axis, a single agent reaching down into the resources it needs to answer a question or complete a step. When people call MCP the "USB-C for AI," this is the picture. One port, many tools.

What MCP does not do is connect that agent to another agent. It has no concept of a second autonomous system with its own goals. It is the plumbing between one model and the world of tools beneath it, and within that scope it has become the default. By 2026 thousands of MCP servers exist, from local file systems to enterprise data platforms.

Google announced the Agent2Agent protocol in April 2025 and donated it to the Linux Foundation in June 2025, where it became a vendor-neutral open project. At its one-year mark in April 2026, the Linux Foundation reported it had grown from the original 50-odd launch partners to more than 150 organizations, including AWS, Microsoft, IBM, Salesforce, and SAP, with production deployments across the major clouds. A2A answers a question MCP does not touch. How do two agents, built by different teams on different frameworks, work together without either one knowing the other's internals?

A2A treats each agent as a peer that publishes a capability card, a small machine-readable profile describing what it can do and how to reach it. One agent reads another's card, hands it a task, and follows that task through its lifecycle to a result, all over standard HTTP. Neither agent has to expose how it works. A scheduling agent can delegate a pricing question to a finance agent and trust the answer without sharing memory, tools, or model. On this horizontal axis, agents coordinate as autonomous peers across organizational and vendor boundaries.

What A2A does not do is connect any of those agents to the underlying tools and data. It assumes each agent already knows how to do its own job. It governs the handshake between agents, not the resources beneath them. That lower connection is exactly the job MCP was built for.

One protocol points down, the other points across. The table sets them against the dimensions that matter when you decide which one to reach for.

Because they work on different axes, MCP and A2A compose rather than compete. The industry comparison most people reach for is the web stack: HTTP, WebSocket, and gRPC all coexist because they solve different transport problems, and nobody asks which one "won." Agent protocols are settling into the same multi-protocol reality.

A concrete example shows the layering. An operations agent receives a request to investigate a revenue dip. It uses MCP to reach down into the data warehouse, pull the relevant numbers, and read a few internal documents. It then uses A2A to reach across to a specialist finance agent, handing off the question of whether a contract change explains the drop. The finance agent, in turn, uses its own MCP connections to query its own systems, and returns a result. MCP did the vertical work inside each agent. A2A did the horizontal work between them. Take either protocol away and the workflow breaks in a different place.

This is why "MCP vs A2A" is the wrong purchase decision. You are not choosing one. You are deciding where each belongs in an architecture that will likely run both, alongside the warehouse-native assistants and copilots already in your stack. We unpacked how that sprawl of tools fragments meaning in Context Silos: Data Silos Reborn for the AI Era.

Both MCP and A2A are transport. They define how context and tasks move between systems. What neither one defines is what that context means, whether it is current, who is allowed to see it, or whether it can be trusted. Protocol compliance is not the same as context accuracy.

An MCP server can faithfully deliver a table called revenue to an agent without anything in the protocol stating which of your three revenue definitions it represents, whether the figures are reconciled, or whether a row should be masked for the requesting user. An A2A handshake can pass a perfectly well-formed task between two agents while both operate on conflicting definitions of "active customer." The protocols carry the payload correctly and stay silent on whether the payload is right.

This matters more with A2A than with a chatbot, because agents act. A misread definition no longer produces a wrong sentence that a human reviews. It produces a wrong action, delegated across systems and executed before anyone notices. The faster and more interconnected your agents get, the more the quality of the context underneath them decides the outcome. We made the full case for this missing layer in Context Engineering for Enterprise AI.

Dawiso lives in the layer both protocols leave open, the governed context beneath them. It connects to more than 40 platforms and builds one foundation across all of them, a Data Catalog of what exists, a Business Glossary of what each term means, classification of what is sensitive, and Interactive Data Lineage of where everything came from. One definition of "active customer," governed once, instead of a different one inside every tool.

Of the two protocols, MCP is the one Dawiso speaks. Through the Context Layer and its MCP Server, Dawiso serves that governed context to any MCP-compatible assistant or agent, so the agent reads trusted definitions, classifications, and lineage from a layer you own instead of improvising its own. A2A is not something Dawiso implements, and it does not need to be. When an agent uses A2A to coordinate with other agents, the context it passes along was already governed at the source, over MCP. MCP carries it, A2A coordinates around it, and Dawiso makes sure the thing being carried is correct.

So when the choice comes up as "MCP or A2A," the honest answer is "both, and one more thing." The protocols are the transport. The agents are the actors. Governed context is what keeps the system trustworthy, and the protocol that delivers it is MCP.