By SpiderHunts Technologies · · 8 min read
"We already have APIs: why do we need MCP?" It's the most common question we get when scoping an AI project. The short answer: your APIs are built for your code, and MCP is built for AI models. They solve different problems and usually work together. Here's how to think about it.
What a traditional API is designed for
A REST or GraphQL API is a contract for developers. A programmer reads the docs, learns the endpoints, and writes deterministic code that calls them in a known order. The API assumes the caller already knows what it wants. There's no built-in mechanism for a non-developer, or a language model, to discover which endpoint to use and when.
What MCP is designed for
MCP is a contract for AI. Each tool carries a natural-language description and a typed schema, and the model reads them at runtime to decide which tool fits the user's request. The AI doesn't need to be pre-programmed with a call sequence: it reasons about the available tools and chains them itself. That's the difference between "call GET /orders/{id}" and "the user wants their order status, so I'll use the get_order tool."
MCP usually wraps your APIs, not replaces them
In practice an MCP server's tools call your existing REST or GraphQL endpoints internally. You keep your APIs for app-to-app integration, mobile apps and partners; you add an MCP layer on top so AI agents can use the same systems safely. One integration, two audiences.
When to use MCP
Reach for MCP when an AI model or agent needs to choose and chain actions dynamically across one or more systems; when you want one integration reusable across many AI clients (Claude, an IDE, future agents); and when you want a governable tool boundary you can log, gate and audit. It shines for support agents, internal "ask your data" assistants, and multi-step automation.
When a plain API call is the better choice
If your own deterministic code controls the logic, a scheduled job, a webhook handler, a fixed workflow, call the API directly. There's no benefit to routing predictable, non-AI logic through a model. Use MCP for the parts that need judgement; use straight API calls for the parts that don't. Many of our builds combine both.
MCP, function calling and Claude
MCP and function calling (tool use) are complementary, not competing. Tool use is how a model invokes a tool; MCP is how tools are packaged and shared so the same server works across many models and clients. You can also define a custom tool that calls your API directly and let Claude use it: MCP simply makes that integration standard and reusable instead of hand-wired into one app.