MCP vs API for AI agents is mostly a question about who reads the documentation. A REST API is written for a developer who reads the docs once and hard-codes the calls. An MCP server is written for a model that discovers the tools at runtime, reads their descriptions, and decides which to call. Same data underneath. Different reader.
I care about this because hiring is moving into chat windows. A founder asks Claude to "find me two senior React engineers who overlap with Eastern time," and whatever the assistant can reach is what gets considered. Here's how I'd think about MCP and APIs if you're making your hiring stack, or your vendor, reachable by agents.
Key Takeaways
- An API is a contract for code. MCP is a contract for a model: tools with names, descriptions and input schemas the model reads at runtime.
- Most MCP servers are thin wrappers over an existing API. You rarely choose one instead of the other; you add MCP on top.
- For agents, the descriptions are the product. A vague tool description gets called at the wrong moment or not at all.
- Writes need guardrails either way: annotations, confirmation steps, and a human owning anything that commits money or shares personal data.
What changes when the caller is a model
With an API, a developer reads your docs, writes code that calls `POST /leads`, and ships it. The intelligence lives in their code.
With MCP, the model gets a list of tools at runtime and picks. The MCP specification defines three server primitives: tools (functions the model can call), resources (context and data) and prompts (templates). Transport is stdio for local servers and Streamable HTTP for remote ones, where, per the transports page, "each message is an HTTP POST to a single MCP endpoint." Authorization is optional in the spec and based on OAuth 2.1 when you use it.
| REST API | MCP server | |
|---|---|---|
| Who reads the interface | A developer, once | A model, every session |
| Discovery | Docs site, OpenAPI file | Tool list returned by the server |
| Integration cost per client | Custom code per integration | Works in any MCP client (Claude, ChatGPT, Cursor and others) |
| What makes it good | Stable endpoints, clear errors | Precise tool names, honest descriptions, tight schemas |
| Risk | A bug in someone's integration | A model calling a write tool in the wrong context |
Anthropic's December 2025 announcement counted more than 10,000 active public MCP servers. That number is why vendors bother: one server reaches every client that speaks the protocol.
The hiring example
We built Ruzora's MCP server on our existing backend. It's listed in the Claude and ChatGPT directories. The read tools return our open roles and information about how we work. The write tools submit a hiring inquiry, a demo request, a candidate application or a project inquiry, and a person follows up. That's it. Each tool has a title, a description and annotations that say whether it reads or writes.
Writing those descriptions taught me more than writing the code. "Submit a hiring inquiry to Ruzora. Use when a company wants to hire senior LATAM engineers for their team" tells the model when to call it. A description like "create lead" would get called when a candidate is asking about jobs, which is a different tool entirely.
We also mark which tools only read and which create records. Directory reviewers read those annotations and compare them with what the tool really does. A tool that says it's read-only and then writes a row is exactly the mismatch they look for, and they should.
When you still want a plain API
- High-volume, deterministic integrations. Your ATS syncing candidates nightly doesn't need a model choosing tools. A REST call is cheaper and easier to test.
- Strict workflows. If the order of steps is fixed by contract or law, encode it in code. Don't hope the model follows the description.
- Anything that commits money or reveals identity. Expose a request, not the commitment. Beeline put it well when it added MCP to its vendor management system: "High-stakes decisions, like classification and compliance, stay in human hands."
My rule: API for systems, MCP for assistants, and a human for the decision. The protocol question one level up, agent to agent, is covered in A2A vs MCP.
A Concrete Version
A founder at a 25-person health-tech company asks Claude, with three connectors on, to help hire a senior React Native engineer.
- Over MCP, about 2 minutes: Claude reads the open req from the company's ATS (Ashby's official MCP server is in Claude's connector directory), calls a staffing vendor's server to check that React Native and US-hours overlap are covered, and submits an inquiry with the founder's name and company.
- Over a plain API, zero minutes of the founder's time but two days of a developer's: someone writes the integration to push the req into the vendor's intake API and parse the response.
For one hire, MCP wins easily. (If you run Greenhouse instead, see what the Greenhouse MCP server does.) For a company placing 200 contractors a year through a vendor management system, the API integration pays back, and nobody wants a model deciding which endpoint to hit at 2 a.m.
Then the human part: within 72 hours the founder sees blind profiles. After a call and a signed agreement, they meet and interview the engineers. No protocol touches that.
The Honest Counterpoint
MCP adds a layer you have to maintain, and the spec moves. The 2026-07-28 revision removed the `initialize` handshake and sessions and deprecated features some servers relied on. If you built in 2025, you're probably updating something.
And agents still fail in boring ways: they call the wrong tool, retry a write, or fill a required field with a guess. An API with strict validation catches more of that than a friendly tool description. If you can't afford a wrong call, don't expose the action to a model at all.
Frequently Asked Questions
Is MCP replacing APIs for AI agents?
No. Most MCP servers wrap an existing API. MCP standardizes how a model discovers and calls those capabilities, so one server works across clients instead of one custom integration per assistant.
What is the main difference between MCP vs API for AI agents?
Who reads the interface. An API is read by a developer who writes code against it. An MCP server is read by the model at runtime, which chooses tools based on their names and descriptions.
Should a hiring vendor offer MCP or an API?
Both, for different buyers. MCP reaches people using Claude or ChatGPT today. An API serves companies that integrate a vendor into their ATS or procurement system. See agentic procurement for the second case.
The Bottom Line
Use an API when code is the caller and the workflow is fixed. Add MCP when a model is the caller and the user is a person typing a request in plain English. Keep the commitments human either way. If your assistant is already looking for engineers, see our vetted bench 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.
