What Are MD Files (and Why Everyone Uses Them for AI Context)?
If you've spent time around AI tools lately, you've seen them everywhere: `CLAUDE.md`,...
If you've spent time around AI tools lately, you've seen them everywhere: CLAUDE.md, AGENTS.md, README.md, a context.md someone keeps pasting into ChatGPT. MD files have quietly become the default way people give AI tools context about themselves and their work. Here's what they actually are, why they caught on, and where they start to break down.
What is an MD file?
An MD file is a text file that uses Markdown, a lightweight syntax for formatting plain text with simple symbols. A # makes a heading, - makes a bullet, **bold** makes bold. The file stays human-readable as raw text while still rendering cleanly when a tool displays it, following a standardized syntax any tool can parse the same way.
The format was created in 2004 to make writing for the web as easy as writing an email. It caught on far beyond that: developers write documentation in Markdown, note apps like Obsidian store everything as .md files, and now AI tools read context from them. The appeal is its simplicity — no proprietary format, no special software, just text in a file you can open anywhere.
Why do people use MD files for AI context?
Because they're the path of least resistance for telling an AI who you are and how you work. Instead of re-explaining your background every session, you write it once in a file and feed that file to the tool. A few reasons they became the default:
- Every tool can read them. Plain text is universal. Claude, ChatGPT, Cursor, and every coding agent can ingest a Markdown file with no conversion.
- You own them. It's a file on your disk. No vendor lock-in, no subscription, no format that disappears if a company shuts down.
- They're human-readable. You can open, edit, and understand the file yourself — unlike a database or an opaque memory store.
- They're version-controllable. Drop them in Git and you get history, diffs, and the ability to track how your context evolved.
For coding agents specifically, this became a formal pattern: CLAUDE.md for Claude Code, AGENTS.md as a cross-tool standard. The agent reads the file at the start of every session and follows what's in it.
What goes in an MD file for AI context?
Whatever the AI needs to know that it can't infer. In practice, that falls into a few buckets:
- Who you are — your role, your company, your background
- How you work — preferences, conventions, standards, tone
- What you're working on — current projects, goals, constraints
- Project specifics (for coding) — build commands, architecture, conventions the agent can't read from the code
The discipline that makes them work is keeping them tight: only what the model genuinely can't figure out on its own. Bloated files full of obvious information waste the context window and bury the signal.
Where do MD files start to break down?
When one file becomes many, and writing them becomes a standing chore.
The single-file approach is clean. The reality rarely stays single-file. You end up with a CLAUDE.md for one project, a different one for another, a personal-context file you paste into ChatGPT, a separate one for Cursor. Each is maintained by hand. Each drifts out of date the moment your situation changes — a project wraps, your role shifts, your priorities move — and stale context is worse than none, because the AI applies it confidently.
Then there's the delivery problem. A file on your disk doesn't automatically reach Claude.ai in your browser, or ChatGPT, or a web-based tool. You're back to copying and pasting, or setting up integrations to bridge the gap. The portability that made MD files appealing only goes as far as your willingness to keep moving them around.
And they don't update themselves. The most useful context — what you're working on right now — is exactly the part that changes fastest and is hardest to keep current in a hand-maintained file. So the file that was supposed to save you from re-explaining yourself becomes a thing you re-write instead.
What's the alternative to maintaining MD files by hand?
Keep the plain-text portability; drop the manual upkeep. This is what a context layer does. Instead of you writing and rewriting files, it extracts your context from the sources where it already lives — your email, calendar, docs, projects — keeps it current automatically, and serves it to any AI tool through MCP.
You still get the things that made MD files appealing: it's structured, you own it, you control what each tool sees. What you lose is the chore — the hand-editing, the per-tool copies, the staleness. For a single project with stable context, an MD file is perfectly good and you don't need anything more. For keeping an accurate picture of yourself across many tools that changes week to week, a file you maintain by hand is the wrong tool for the job, and a context layer is what replaces it.
→ The alternatives in depth: MD File Alternatives for AI Context
→ How a context file works for coding agents: What Is a Context File?
→ Replace your hand-maintained context files with Unabyss →
