Connection types:
API, MCP and CLI
How systems talk to each other, and how AI joins the conversation. Three connection types cover almost everything.
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Connecting two different systems together is an integration.
Your CRM talking to your email tool, your website talking to your payment processor, or your AI connecting to, well, anything. These are examples of connecting different systems to add functionality or access data, the two main reasons systems are connected. There are three main connection types used in AI workflows: API, MCP and CLI.
Every connection carries an identity and a scope.
Who is asking is authentication; what they are allowed to see and do is authorization. Together they are why a connection to your systems can be safe, and they apply to API, MCP and CLI alike. Each connection type embeds the three parts, the address, the auth, and the instructions, in a different way.
A menu of tools.
An API is the published specification of a system’s tools: the addresses, request formats and responses. It was architected for deterministic code, where you needed to be exact in the call and the response.
The customer record 1042 comes back as structured data, the same fields in the same places, every time.
Structured tools for specific tasks.
Where APIs shine
Connecting to a system repeatedly to get standard data out of it. They were designed for deterministic code that needs repetitive inputs and responses.
Where AI comes in
An AI agent can read and use API calls through the tools available when auth and access are structured correctly. Auth here requires special attention: do not hand the keys to the AI.
An AI-friendly wrapper around an API.
MCP, the Model Context Protocol, wraps an API in a form built for models. It allows discovery, it carries its own how-to-use-me instructions, and it allows secure user authentication the model does not see. With a raw API, each of those is a separate piece of effort that needs to be built.
Handed the wiring, or bundled with it.
API
The model can call it directly, but someone hands it the address, the format, and the key first. Precise and fast, with setup work for every system it talks to.
MCP
Discovery, instructions, and authentication come bundled. The model connects, reads the menu, and starts using the tools, and new tools on the menu need no new wiring.
Same API underneath. MCP bundles the menu, the manual, and the key.
Understanding MCP connections.
When an AI connects to an MCP server, the user will already have set up the address and the authentication. On connecting, the AI receives a manifest of the server’s tool list; each tool with a name and a description with its inputs and outputs. The design of those tool descriptions is very important for effective AI use.
Local toolsets agents can use.
A CLI is a program driven by typed commands in a terminal, originally designed for developers, but since it runs on text commands an AI can run it too. A CLI is local to the machine it runs on, not necessarily your machine: it shows up wherever an agent has a terminal, in developer-oriented systems such as Claude Code, on a laptop or in a cloud sandbox. Very useful in building and deploying tools, but not part of the tool itself. Address, auth and instructions are often baked into the CLI on install or login, which can make it the most inscrutable of the three.
API, MCP and CLI each have a place in the tool kit.
Knowing which to use depends on what the system supports, your goals, and the architecture and security requirements.