What is MCP?
Model Context Protocol is an open standard that lets AI models talk directly to external tools, data sources and systems. It was introduced by Anthropic in late 2024 and has since been adopted across the AI industry. MCP gives AI models one standardized way to act and pull in information mid conversation.
This offers a unique advantage compared to how AI usually works. Normally, you hand a model a fixed set of facts upfront, and that is all it has to work with. MCP changes that. The AI can reach out to your business systems live, while the conversation is happening, and get an answer back in real time.
MCP is new and evolving fast. The rules for how it works, how it handles permissions, and tools built around it are all likely to change as the ecosystem matures.
What is behind an MCP Connection?
- The user asks for what they need in plain language.
- The MCP server picks the right tools: the actions the assistant is allowed to perform.
- Onit acts on your data, then sends the results back into the conversation so the AI can respond right away.
API vs MCP: Choosing the Right Integration
APIs and MCP aren't competitors, they solve different problems. The right choice comes down to who's driving the integration.
Use a traditional API when:
- A developer is building a defined workflow with known inputs and outputs – the logic is mapped out ahead of time.
- You need system-to-system automation on a schedule or event trigger - anything that fires whether or not a human is in the loop
- You're moving or syncing bulk data between platforms (ETL, reporting pipelines) - high volume, repetitive data movement where consistency matters more than flexibility.
- The integration is fixed, with no dynamic decision-making required – if the same input always produces the same call, there is nothing for an AI to reason about. Building that with MCP would just be adding overhead for no benefit.
Use MCP when:
- An AI agent needs to act or retrieve data dynamically, mid-conversation – the AI isn’t following a pre-built path, it’s pulling in live information as the conversation unfolds and adjusting based on what it gets back.
- The end user (not a developer) drives what happens next, in natural language – The person says what they need and the AI figures out which tool answers it.
- The AI has to decide at runtime which tools to call based on context and intent – giving a model the judgment to pick the right tool for the moment, rather than being told which one to use in advance.
- You want to prototype a new capability fast, before committing to a full build – MCP lets you test whether a use case is worth investing in before you spend time on a fully engineered API integration.
Most enterprise deployments end up using both: APIs for background automation, MCP for AI-driven, user-facing actions.
Key Takeaways
APIs will keep doing the heavy lifting in the background – that does not change. MCP adds the ability for an AI to ask, decide, and act in the moment, without a developer having to predict every question in advance. As the standard matures, the real skill isn’t picking one over the other, it’s knowing which job each one is built for.
