MCP vs A2A: How AI Agents Connect to Tools and to Each Other
MCP connects AI agents to tools and data; A2A connects agents to each other. What each protocol does, how they differ, and when a startup needs them.
An agent is only as useful as what it can reach. A support agent that can't see orders, or a sales agent that can't write to the CRM, is just a chatbot with ambitions.
Until recently, every connection was a custom integration: one for this model framework, another for that one, rebuilt each time you switched. Two open protocols are changing that. MCP standardises how agents connect to tools and data. A2A standardises how agents talk to other agents. They solve different problems, and most teams only need one of them today.
The problem they solve
Imagine three AI applications (an internal assistant, a support agent, and a coding tool) that each need access to the same five systems. Without a standard, that's fifteen integrations to build and maintain. With a shared protocol, each system exposes itself once and every compatible application can use it.
Model Context Protocol (MCP): agents to tools
The Model Context Protocol, introduced by Anthropic and now supported across most major AI tools, defines a standard way for an AI application (the client) to connect to an MCP server that exposes capabilities. A server can offer:
- Tools: actions the model can call, such as
create_ticketorsearch_orders. - Resources: data the application can read into context, like files, records, or documents.
- Prompts: reusable templates for common tasks.
Example: you build one MCP server in front of your helpdesk. Your support agent, your team's desktop AI assistant, and your developers' coding tools can all use it without separate integrations.
What MCP doesn't solve for you
- Bad tool design. A vague tool description confuses the model whether it's served over MCP or not.
- Permissions. You still decide which user's credentials a call runs with and what it's allowed to change.
- Trust. Installing an MCP server from an unknown source is installing code that can act on your behalf. Review it like any dependency.
- Prompt injection through tool results. Text returned by a tool (an email body, a web page) can contain instructions. Treat it as data, not commands.
Agent2Agent (A2A): agents to agents
The Agent2Agent protocol, launched by Google and now an open project, addresses a different situation: one agent needs another agent's help, and the two may be built on different frameworks or run by different organisations.
With A2A, an agent publishes an Agent Card describing what it can do and how to reach it. Another agent can discover it, send it a task, and receive progress updates and results through structured messages. The calling agent doesn't need to know how the other one works inside.
Example: your procurement agent needs a quote. Instead of scraping a supplier's site, it sends a task to the supplier's own quoting agent, which replies with prices and availability.
MCP vs A2A at a glance
| MCP | A2A | |
|---|---|---|
| Connects | An AI application to tools and data | An agent to another agent |
| The other side is | A tool: it does what it's told | An agent: it reasons and may push back |
| Typical interaction | Call a function, get a result | Hand off a task, get updates and a result |
| Best fit | Reusing integrations across agents and AI apps | Agents across teams, vendors, or frameworks |
| Startup priority | Often useful now | Usually later |
They aren't competitors. An agent might use MCP to reach its own tools and A2A to delegate part of a job to someone else's agent.
Do you need either one?
Use MCP when
- More than one agent or AI tool needs the same system.
- You want your team's off-the-shelf AI assistants to reach internal data safely.
- You expect to switch models or frameworks and don't want to rebuild integrations.
Skip MCP (for now) when
- You have one agent with three tools. Plain function calling is simpler and easier to debug.
Use A2A when
- Agents owned by different teams or companies need to cooperate.
- You're exposing your own agent as a service other businesses' agents can call.
For most startups, the honest answer is: build tools as clean functions first, wrap the ones you reuse in an MCP server, and revisit A2A when a real cross-agent use case appears.
How we use them
In our AI agent development projects we keep business logic in ordinary, well-tested functions and expose them through whichever interface fits: direct tool calls, MCP, or an API. Model access goes through AiKey, our AI gateway, so swapping providers never means rewriting tools. That keeps the agent portable as both the protocols and the models evolve. For the bigger picture, read what an AI agent is or the full AI agent development process.
AI agent development services
We scope, build, and monitor production AI agents for startups, with guardrails and evaluation built in.
Explore AI agent development servicesFrequently asked questions
What is the Model Context Protocol (MCP)?
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An open standard that lets AI applications connect to external tools, data, and prompt templates through MCP servers, so one integration can be reused by any compatible client.
What is the difference between MCP and A2A?
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MCP connects an agent to tools and data sources that simply execute requests. A2A connects an agent to other agents that reason independently, letting them discover each other and collaborate on tasks.
Is MCP secure?
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MCP is a protocol, so security depends on how you use it: review servers before installing them, scope credentials narrowly, require approval for risky actions, and treat tool output as untrusted data.
Does a startup need A2A?
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Usually not at first. A2A becomes valuable when agents from different teams, vendors, or frameworks must work together. Most early agents only need tools, which MCP or plain function calling covers.