What you’ll get from this lesson: Distinguish switching models from making tools available to other AI applications, and understand the conditions for reusing your assets.
Prerequisites: No software installation required. Prepare a work example that can be shared publicly or has had sensitive information removed.
I do not want to redo all the information and tools I have organized every time I switch AI. In the original recording, I drew two directions. One is for my own Agent to switch to a different model; the other is for other AI applications to use functions I have already built through an interface.
Start with Two Different Directions
The first direction is your own Agent calling a model. When the model-related part changes, you continue to organize and maintain your data and tools. An actual switch still requires checking the model interface, tool formats, and results.
The second direction is external AI using your tools. For example, if you already have a function that organizes spreadsheets, you can later evaluate whether to provide a controlled interface that lets compatible external tools call it. This allows you to reuse existing functionality without moving the entire system into every chat window.
An API Is an Interface; MCP Is a Protocol for Connecting AI Applications
An API is an interface for programs to exchange data and call functions. MCP defines a protocol for collaboration between AI applications and external servers, including enabling clients to discover and call tools, access resources, and more.[3]
MCP can therefore be one way to implement an integration; it should not be described as the next generation of APIs or as another name for an API. You still need to confirm whether both ends support it, how the functions are described, who can use them, and how much data can be returned.
Understand Reuse Through a Single Query Tool
Take the task board from the original draft as an example. External AI first checks the current to-do items and progress, then answers questions based on what it found. If only query permissions have been granted, it should not also modify tasks, delete records, or transfer all the data elsewhere.
This is a workflow illustration for teaching, not a claim that L0 already provides a task board you can connect to. Even if you currently have only one work document, you can use it to understand the value of “providing information from the same source.”
Check Four Things Before Switching Access Points
First, confirm that the connection works, then check whether the retrieved data is correct. Next, verify that unauthorized actions are blocked. Finally, check whether the results can be traced back to the original data or tool records.
If a new tool does not support your current interface, evaluate whether the extra integration work is worthwhile. Keeping your own assets can reduce how much needs to be redone; it does not mean you will never need to make changes or run tests.
Today, Draw Your Own Two Arrows
On paper, draw “my own Agent → model,” followed by “external AI → controlled interface → my own tools.” Choose one function you genuinely want to reuse, and mark what it is allowed to read and what it is prohibited from doing.
You do not need to predict which AI will be the most powerful, and you do not need to set up a server at L0. First understand how data, tools, and rules can be maintained separately. That makes it easier to see why what you are building up now has value.
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Sources Checked
[3] Model Context Protocol, “Architecture overview.” Checked on 2026-09-11. For compatibility and security limitations, see the source verification materials in this package.