A2A vs MCP is the wrong fight. The A2A project's own docs put it in one line: "A2A connects the agents to each other", while MCP connects each agent to its own tools. If you're a CTO wondering which one your hiring stack needs, the honest answer is that they sit at different layers, and the hiring use case shows the split better than most demos.
I run a staffing company with an MCP server listed in the Claude and ChatGPT directories, so the first visitor on our side is sometimes software: an assistant reading our site, or a connector calling our tools on a buyer's behalf. Here's how the two protocols look from the receiving end.
Key Takeaways
- MCP connects one agent to tools and data. A2A lets two independent agents, often owned by different companies, hand work to each other.
- In hiring, MCP fits "my assistant searches a vendor's roles or my ATS." A2A fits "my recruiting agent asks a staffing vendor's agent for a shortlist and waits for the answer."
- Both now live under the Linux Foundation's Agentic AI Foundation, so the "which standard wins" question matters less than it did in 2025.
- Neither protocol vets a candidate. They move requests. The judgment about who is real and who is good still has to happen somewhere.
What each protocol actually does
MCP came first. Anthropic introduced it on November 25, 2024 as a standard for connecting AI assistants to the systems where data lives. An MCP server exposes tools, resources and prompts. The agent calls them. The server doesn't think; it does what it's asked and returns a result. The current spec revision is 2026-07-28, which made the protocol stateless.
A2A came from Google, announced April 9, 2025 as a protocol that "complements Anthropic's Model Context Protocol." The Linux Foundation took it on in June 2025, and the spec reached version 1.0 in March 2026. The unit of work in A2A is a task. One agent sends a message, the other agent works on it, and the task moves through states like submitted, working, input-required and completed. The remote agent is opaque. You don't see its tools, only its answers.
| MCP | A2A | |
|---|---|---|
| Connects | An agent to tools and data | An agent to another agent |
| Who decides | The calling agent | Each agent decides for itself |
| Unit of work | A tool call | A task with a lifecycle |
| Discovery | Server lists its tools | Agent Card at /.well-known/agent-card.json |
| Long-running work | Possible, but not the core shape | Built in: streaming, push notifications |
| Hiring example | Read open roles, submit an inquiry | Ask a vendor agent for a shortlist, answer its follow-up questions |
Governance converged too. Anthropic donated MCP to the Agentic AI Foundation in December 2025, and A2A joined the same foundation in August 2026. Same neutral home. Different jobs.
A2A vs MCP in a hiring workflow
Picture a recruiting agent inside a 40-person company. It has three jobs: read the hiring plan, find candidates, and keep the hiring manager informed.
For the first two it uses MCP. It reads the job from your ATS through an MCP server. Greenhouse and Ashby both ship official ones now (I wrote up what the Greenhouse MCP server does). It might also call a vendor's public MCP server to check what roles they cover. These are tool calls. Fast, synchronous, predictable.
Then it needs a staffing vendor to propose engineers. This is where MCP starts to strain. Sourcing three vetted senior engineers isn't a function call that returns in 200 milliseconds. The vendor will have questions: which timezone overlap do you need, is the budget firm, can the person start in two weeks. Some answers take a day. That back-and-forth is exactly what A2A's task model was designed for, including the input-required state where the remote agent pauses and asks.
So the realistic stack in 2027 is both. MCP inside each company, A2A between them. I explain the discovery piece in this A2A agent card example.
Where Ruzora sits today
Our MCP server is live and listed in the Claude and ChatGPT directories. An assistant can read our open roles and company information and submit a hiring inquiry, and then a person (me) follows up. That covers the "my assistant talks to a vendor" case for most buyers right now.
We also run an A2A endpoint now, for buyers whose own agents want to delegate. Our Agent Card is live at mcp.ruzora.com/.well-known/agent-card.json, with one JSON-RPC interface on A2A 1.0. Another company's agent can search blind profiles of vetted engineers, verify a Ruzora Vetted badge, or request a vetted shortlist, and when a required detail is missing the task pauses in input-required and names the field. A shortlist request still lands with a person on our side; the endpoint makes asking cheaper, not the vetting faster.
What it doesn't do is replace the steps that make a shortlist trustworthy. Every engineer on our bench has passed a graded coding challenge and an AI interview answered on camera, scored separately. You see blind profiles first, we sign the service agreement, and then you meet the engineers with their full identity. No protocol shortcut changes that order, and I don't think one should.
A Concrete Version
A Series A fintech wants two senior Python engineers. Its ops lead uses Claude with three connectors.
1. Monday 9:00. The assistant reads the two open reqs from Ashby over MCP. Two tool calls, under a second each.
2. Monday 9:02. It calls a staffing vendor's MCP server, confirms they cover Python and US-hours overlap, and submits an inquiry with the company name and contact. One more tool call.
3. Monday afternoon. A human at the vendor replies. In an A2A world, this step would instead be the vendor's agent opening a task, asking two clarifying questions (budget band, start date), and holding in input-required until the buyer's agent answers.
4. Within 72 hours. A vetted shortlist arrives as blind profiles. The hiring manager picks who to meet. After a short call and a signed MSA, interviews happen with full identities, and onboarding takes about two to three weeks.
MCP saves maybe an hour of form-filling here. A2A would save the email ping-pong in step 3. Neither touches step 4, which is where hiring actually succeeds or fails.
The Honest Counterpoint
Most companies don't need A2A yet. If your assistant only reads your own systems and fills in a vendor form, MCP covers it, and adding an A2A layer is architecture for its own sake. Gartner predicted in June 2025 that over 40% of agentic AI projects will be canceled by the end of 2027. Some of those will be multi-agent systems built before anyone had a second agent to talk to.
There's also a trust gap A2A doesn't close by itself. An Agent Card tells you what an agent claims it can do and how to authenticate. It doesn't tell you whether the engineers behind it are vetted, or whether the company honors its contract. You still check that the old way.
Frequently Asked Questions
Is A2A vs MCP an either-or choice?
No. The A2A docs describe them as complementary: MCP for an agent's tools, A2A for agent-to-agent collaboration. A recruiting agent would use MCP to read your ATS and A2A to delegate a sourcing task to a vendor's agent.
Which should a staffing vendor support first?
MCP, because assistants like Claude and ChatGPT can use it today through their connector directories. A2A matters once buyers run their own recruiting or procurement agents that delegate tasks to outside agents.
Can an AI agent hire an engineer on its own over A2A or MCP?
It can request, compare and schedule. It shouldn't sign the contract or make the final call. Someone has to meet the engineer and own the decision. See what an AI hiring agent should do.
The Bottom Line
MCP is how an agent uses tools. A2A is how agents work with each other. In hiring, you'll want MCP now and A2A when your agents start delegating to other companies' agents. Either way, the protocol carries the request; it doesn't vet the engineer. If you want a shortlist where that part is already done, see our vetted engineers or read how we vet.
Roberto Espinoza is CEO of Ruzora, which helps US startups hire pre-vetted senior LATAM engineers, with a vetted shortlist in 72 hours. See available engineers.
