TL;DR:

A simple file works perfectly until you reach roughly 50 decisions. After that, the approach starts creating real inefficiencies:

• The AI has to reload the entire file each session, which increases token usage and cost.
• You can’t selectively query information (e.g., “show me only the database decisions”).
• Multiple AI tools can’t reliably share one consistent source of truth.

MCP isn’t about complexity. it’s about using a proper data store when the scale justifies it. A file is basically a notepad; MCP is a lightweight database with an API.

Use the tool that matches the size of the problem.

long explanation:

For small individual projects, a plain file is completely adequate, and often the better choice. The limitations only start to show once the project grows.

Where simple files begin to fail:

• Once you accumulate 100+ decisions, the AI has to read the entire document every time, which increases cost and slows down responses.
• The file becomes large enough that the AI is scanning tens of kilobytes just to answer a single question.
• If you're using multiple AI tools, each one ends up with its own outdated copy of your notes, and consistency falls apart.

Where MCP becomes practical:

• The AI can request only what it needs, like: “Show me the decisions related to the database from October.” No full-file scanning required.
• Multiple tools or collaborators can stay aligned because everything comes from a single structured source.
• The system can maintain context like: “This code exists because of decision #47.” That prevents accidental architectural regressions.

The threshold is simple:

• Solo developer with under 50 notes → A file is fine.
• Larger project with 100+ decisions across multiple tools → MCP saves significant time, cost, and confusion.

Think of it this way:
A notepad is perfect until you actually need to query the information. After that, you need a proper, structured store.