MCP vs. APIs: What's the difference?
Model Context Protocol (MCP) and application programming interfaces (APIs) both act as digital bridges that allow connection between separate systems. MCP capabilities are built on top of APIs, and the former wouldn’t exist if not for the latter.
The technologies differ in how they function and what they were built to serve:
- MCP connects language models to external tools and data, powering workflows that adapt and improvise in real time.
- Traditional API workflows follow a fixed set of rules. Once 2 systems are connected, the interactions are limited to a specific set of actions programmed by a developer.
Should I use MCP or an API?
If your application doesn’t use a large language model (LLM), stick with an API—it’s faster and simpler. If you’re connecting 1 LLM to 1 service you control, you can also use an API with the LLM’s native function calling (a built-in feature in LLMs that formats the model’s output into a runnable command).
Reach for MCP when the same tool needs to work across multiple AI applications, when you’re using tools you didn’t build, or when you want a model to discover new capabilities at runtime.
What is MCP?
MCP is an open source protocol that standardizes how AI applications connect to external tools and data. Think of it as a USB-C cable that connects devices to accessories and allows the transmission of data.
Before MCP, developers had to create custom API integrations for specific use cases. This meant they were rewriting the same integrations many times in slightly different ways. Each connection between an AI application and an external service was made to order, which was extremely time consuming.
4 principes clés à prendre en compte pour mettre en œuvre des technologies d'IA
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