AI Documentation Tools: How to Generate and Publish Docs with AI

AI coding assistants like Claude, Copilot, and Cursor can generate documentation directly from your codebase. They read your functions, types, and project structure, then produce markdown files that describe what your code does. The part most workflows skip is publishing: getting that generated markdown somewhere people can actually read it.

What AI Is Good at Generating

AI assistants produce solid first drafts for certain types of documentation. Here’s where they do well:

API documentation from code. Point an AI at your route handlers or function signatures and ask for API docs. It will pull out parameters, return types, and error cases. The output is usually 80-90% correct, which is a lot faster than writing from scratch.

Prompt: "Generate API documentation for all routes in src/routes/api/"

READMEs from project structure. AI can scan your package.json, directory layout, and config files to generate a README with installation steps, project structure, and usage examples.

Prompt: "Write a README for this project. Include setup instructions,
the tech stack, and how to run tests."

Changelogs from git history. Give an AI your recent commits and it will group them by type (features, fixes, chores) and write human-readable release notes.

git log --oneline v1.2.0..HEAD | claude "Write a changelog entry for these commits"

Code comments and docstrings. AI writes JSDoc, Python docstrings, and inline comments that explain what functions do. This is one of the most reliable use cases because the AI has the code right in front of it.

Where AI struggles: architecture decisions, onboarding guides that require context about team processes, and anything that needs to explain why something was built a certain way. Those still need a human.

The Publishing Problem

Generating a markdown file is half the job. The other half is getting it where readers can access it. Most AI-generated docs end up in one of these places:

Committed to the repo. The AI writes a .md file, you review it, and it lives in your docs/ folder. Anyone with repo access can read it. But if you want to share it with people outside the repo (stakeholders, clients, other teams), they need to be added to the repo or you copy-paste the content somewhere else.

Pasted into Notion or Confluence. You copy the AI output and paste it into your team wiki. This works but it’s manual. Every time the docs update, you paste again. Formatting often breaks in the transfer.

Published with MDtoLink. The AI generates markdown, you run one command, and it’s live at a URL. No build step, no wiki, no copy-pasting.

mdtolink publish docs/api-reference.md
# => Published to https://app.mdtolink.com/d/abc123

The Full Workflow

Here’s what a complete AI docs workflow looks like, from generation to a shareable URL:

Step 1: Generate

Ask your AI assistant to write the docs. Be specific about format and audience.

Prompt: "Write API documentation for the /api/documents endpoints.
Format as markdown with H2 headers for each endpoint.
Include request/response examples. Target audience: developers
integrating with our API."

Step 2: Review

AI-generated docs need a human pass. Check for:

• Accuracy. Does the documented behavior match what the code actually does? AI sometimes hallucinates parameters or return values.
• Completeness. Did it miss any endpoints, error cases, or edge cases?
• Tone. Is it written for the right audience?

Save the reviewed markdown to a file:

# Save the AI output after review
cat > docs/api-reference.md << 'EOF'
# API Reference
...
EOF

Step 3: Publish

Get the docs online with a single command:

mdtolink publish docs/api-reference.md --title "API Reference"

That’s it. You have a URL you can drop in Slack, add to your README, or send to a client. When the docs need updating, regenerate with AI, review, and publish again.

Hands-Free Publishing with MCP

If you’re using Claude Code or another MCP-compatible AI tool, you can skip the manual publish step entirely. The MDtoLink MCP server lets your AI assistant publish directly.

# Add the MCP server to Claude Code
claude mcp add mdtolink \
  -e MDTOLINK_API_KEY=mdtolink_xxxxxxxxxxxx \
  -- npx -y -p mdtolink mdtolink-mcp

Now you can tell Claude Code:

"Generate API docs for the /api/documents endpoints and publish them to MDtoLink."

The AI generates the markdown, calls the publish tool through MCP, and gives you the URL. One prompt, zero manual steps between generation and publishing.

You can also update existing documents:

"Update the API docs at document ID abc123 with the new /api/teams endpoints."

The AI fetches the existing document, adds the new content, and publishes an update to the same URL.

Automating the Cycle

For teams that want docs to stay current without manual work, combine AI generation with CI:

1. A scheduled GitHub Action runs weekly (or on every release).
2. The action uses an AI assistant to regenerate docs from the current codebase.
3. The output is published to MDtoLink via the CLI.
4. Team members access docs at stable URLs that always reflect the latest code.

This works best for API references and changelogs, where the source of truth is the code itself. For conceptual docs and tutorials, you still want a human in the loop.

Choosing the Right Approach

Match the tool to the situation:

The trend is clear: AI handles the first draft, humans handle the review, and the publishing step should be as close to zero-friction as possible. For more on AI-powered workflows, see the AI agent reports use case.