MCP vs API isn't really a contest. An API is how software talks to a service. MCP is how an AI app, like Claude or ChatGPT, discovers and uses tools on its own. Most MCP servers are thin wrappers around an API. Call the API directly when your own code is the caller. Add MCP when you want AI assistants to use it.
The short version
| API (REST, GraphQL) | MCP | |
|---|---|---|
| Who calls it | Your code | An AI app, deciding by itself when to use a tool |
| How the caller learns what's available | Docs, or an OpenAPI description | It asks the server at connection time (tools/list) |
| Message format | Varies by API | Always JSON-RPC 2.0 |
| Where it runs | On the service's own servers | On your machine (stdio) or remotely (Streamable HTTP) |
| Works with many AI apps at once | Only with a custom connector for each | Yes, any MCP-compatible app |
| Best for | Apps and backends you write | AI assistants and agents |
One task, both ways
Say you want an AI assistant to open GitHub issues for you.
With the API directly, you read GitHub's API docs, set up authentication, and describe a "create issue" function in the format your model provider expects. Then you write the code that runs when the model calls it. It works well, but only inside the app you built. To do the same thing in Cursor or ChatGPT, you'd rebuild the glue for each one.
With MCP, you connect a GitHub MCP server to the AI app you already use. The app asks the server what it can do, sees an issue-creation tool, and uses it when you ask. Connect the same server to another MCP app and it works there too, with no new code.
Notice what didn't change: the GitHub MCP server still calls the GitHub API underneath. MCP didn't replace the API. It packaged the API so any AI app can pick it up.
When to call the API directly
- Your own backend or script is the caller, and no AI is involved.
- You need exact control over every request, retry and error.
- You're building one fixed integration for one app, and an SDK already does the job.
- Performance matters, and an extra layer between you and the service isn't worth it.
When MCP is the better choice
- You want an AI assistant to decide on its own when to use a tool.
- The same capability should work in several AI apps. As of September 2026, Claude, ChatGPT, Cursor and VS Code all support MCP.
- A good MCP server already exists for the service, so you write nothing.
- You want people who don't write code to connect tools themselves.
If you're unsure, try both on one small task. An hour with a prototype will tell you more than a comparison table.
What MCP doesn't solve for you
MCP makes connecting easier. It doesn't handle three things you still have to think about:
- Permissions. The server still uses real credentials. Give it an account that can only do what the AI needs, like a read-only database user.
- Which tools the AI may use. Most servers expose everything they can do, including tools that delete things. Hiding a tool from the list isn't enough on its own. Calls to it must be refused too.
- Running the server. Someone has to install it, keep it updated, and store its secrets safely. On your laptop that's you. For a team, it's usually a shared service.
Where MCPifex fits
We built MCPifex for those last two problems. You pick a server from the marketplace and save its credentials in the portal. Then you choose which tools are enabled. Write and delete tools start switched off, and the gateway refuses calls to anything you didn't enable. We run the server, and you connect any MCP client with one API key. That makes it a remote MCP server you don't operate.
See the PostgreSQL server for a real example, or follow the quickstart to connect your first one. The free plan covers 3 instances.
Verdict
Choose an API when your code is the caller. Choose MCP when an AI is the caller, especially across several AI apps. They work together: the API does the work, and MCP makes it usable by any assistant. New to MCP? Start with What is MCP?
Quick answers
No. Most MCP servers wrap an existing API. MCP packages the API so any compatible AI app can discover and use it.
Call it directly when your own code is the caller, you need exact control over requests and retries, or you build one fixed integration for one app.
No. The server still uses real credentials. Give it a least-privilege account and enable only the tools the AI needs.
Sources
- Model Context Protocol, "What is MCP".
- Model Context Protocol, "Architecture overview".
- Model Context Protocol, Specification (latest).
- github/github-mcp-server, GitHub repository.
- OpenAI, "Function calling", developer docs.
- JSON-RPC Working Group, JSON-RPC 2.0 Specification.
- OpenAPI Initiative, OpenAPI Specification.
Ready to try it?
Run hosted MCP servers behind one endpoint and choose exactly which tools your AI can use.