MCP vs A2A: the difference every company should understand
MCP and A2A are not competitors: they solve different problems. Learn what each protocol connects and how to think about AI agent architecture.
In the world of AI agents, new acronyms appear every few months. Two of the most important are MCP and A2A. At first, they may sound similar because both are about connecting agents. But they solve different problems.
The short version is this: MCP connects agents to tools and data; A2A connects agents to other agents.
MCP: how an agent uses tools
MCP, or Model Context Protocol, was created to standardize how an AI application accesses external sources. Examples include Google Drive, Slack, GitHub, Postgres, a CRM, or an internal tool.
Without a standard, every vendor would need to build different connectors for every model and every interface. MCP proposes a shared layer: the agent can request context or execute actions through MCP servers.
In a company, this supports use cases such as:
- Searching documents in Drive.
- Querying an internal database.
- Reading support tickets.
- Retrieving Slack messages.
- Running an authorized action in a business tool.
MCP answers the question: which tools can this agent use, and how does it access them?
A2A: how agents collaborate
A2A, or Agent2Agent, works at another level. Imagine a company already has several agents: one for support, one for sales, one for operations, and one for finance. It is not enough for each agent to have tools. They also need to coordinate.
A2A aims to let one agent discover, invoke, and collaborate with another agent without needing to know all of its internal details. This makes delegation possible while preserving responsibility boundaries.
For example, a sales agent can ask an operations agent for a delivery estimate. The sales agent does not need direct access to the logistics system. It only needs a reliable answer inside a controlled workflow.
Why they are not rival technologies
The confusion comes from the fact that both pieces appear in agentic architectures. But they sit at different levels:
| Question | More relevant protocol |
|---|---|
| How does the agent access Drive, Slack, or a database? | MCP |
| How does one agent coordinate with another specialized agent? | A2A |
| How do we control which actions AI can execute? | Governance, permissions, and audit |
| How do we avoid answers without sources? | RAG, evaluation, and observability |
In a mature architecture, MCP and A2A can coexist. An agent uses MCP to consult tools and A2A to coordinate with other agents.
What this means for smaller companies
For an SME, the priority is not implementing protocols because they are fashionable. The priority is organizing the architecture:
- First, connect reliable knowledge.
- Then, define permissions and actions.
- Next, add specialized agents.
- Finally, enable collaboration between agents.
If the first step is skipped, agents will collaborate on incomplete or outdated information.
The practical lesson
Do not ask only "do I need MCP or A2A?" Ask:
- Does my agent need external tools and data?
- Do I need several specialized agents?
- Who can see which information?
- Which actions require human approval?
- How do I audit what happened?
The technology matters, but the trust architecture matters more.
How we see it at Polp
At Polp, the initial focus is internal knowledge: documents, permissions, sources, and reliable answers. That is the foundation on which more specialized agents can later operate.
MCP and A2A show where the market is going, but the base remains the same: if a company does not know where its knowledge lives, no protocol will magically turn it into an agentic organization.
For an enterprise knowledge SaaS like Polp, this architecture matters because it lets companies grow from reliable answers into more connected, governed, and useful agentic workflows for SMEs.
Sources:
Stop searching. Start asking.
Upload your PDFs, spreadsheets, and docs. AI handles the rest.
Get started