Is MCP the Next Big Thing in API Development?
• APIL Team • 4 min read
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APIs have been the glue of modern software for decades. Every product that talks to another system does it through some kind of API.
But as AI models become part of the development workflow, that glue is starting to look brittle.
Large language models can describe your endpoints, but they can’t really use them. That’s where a new idea — the Model Context Protocol (MCP) — comes in.
What Exactly Is MCP?
MCP is an open specification that defines how AI models can interact with external tools, APIs, and data sources in a structured way.
Instead of every AI assistant shipping with its own plugin format or brittle integration, MCP gives them a common language for describing what they can do and how they can do it.
You can think of MCP as the API contract between your systems and the AI trying to use them.
Why Developers Should Care
Right now, most AI systems operate in a sandbox. They’re good at reasoning, but they don’t have a safe, standardized way to perform real actions.
MCP changes that by providing a lightweight, secure protocol for connecting a model to your existing tools.
Here’s what that unlocks:
- A common structure for describing capabilities instead of hard-coded plugins.
- Safer execution — the AI can only access what’s explicitly exposed.
- Cross-model support — works with any provider that speaks MCP.
- Reuse of existing work — your OpenAPI or Postman files can become MCP descriptors.
It turns AI from a static question-answer box into something that can actually interact with your systems — within guardrails you define.
MCP vs. Plugins
Plugins were the first serious attempt at letting AI talk to APIs. They worked, but they were tied to one platform at a time — OpenAI, Anthropic, or whoever implemented them.
MCP is different. It’s not a marketplace or SDK; it’s a protocol. It uses JSON-RPC over WebSockets and doesn’t belong to any one vendor.
| Aspect | Plugins | MCP |
|---|---|---|
| Ecosystem | Closed (platform-specific) | Open standard |
| Communication | REST / schema files | JSON-RPC |
| Security | Platform controlled | Sandboxed, developer-defined |
| Portability | Limited | Works across models |
If you’ve ever built a plugin and felt locked in, MCP is the escape hatch.
Where APIs Fit In
The nice part is that you probably already have most of what you need.
OpenAPI and Postman collections describe your endpoints in detail — exactly the information an AI model needs to call them safely.
By translating those definitions into MCP format, you make your API model-ready without touching your backend.
That’s the core idea behind APIL — helping developers move between Postman ↔ OpenAPI ↔ MCP and automatically generating clean documentation along the way.
You don’t have to rebuild your API for AI. Just describe it in a way the model can understand.
Looking Forward
If REST made APIs easy for browsers and gRPC made them fast for services, MCP could be the thing that makes them usable for AI.
We’re still early, but the direction is clear: AIs that can reason and act will need a standardized way to interact with software. MCP is a strong candidate for that role.
Final Thoughts
MCP isn’t just another format—it’s a signal that APIs are entering the AI era.
For developers, it’s worth experimenting with now, before it becomes a baseline expectation.
You can start by converting your Postman or OpenAPI files to MCP at apitrench.com.
The future of APIs isn’t just about routes and responses anymore — it’s about context.
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