Every engineering team has the same documentation problem. Someone writes thorough docs during a launch. Six months later, the API has changed, the configuration format is different, and three pages reference a feature that was renamed. Nobody updated the docs because updating docs is a separate task from writing code, and separate tasks get forgotten.

The usual advice — “make documentation part of your culture” — doesn’t work. Culture doesn’t survive deadline pressure. What works is making documentation updates part of existing workflows so they happen automatically or with minimal friction.

Here are three approaches that actually reduce doc staleness, each targeting a different part of the problem.

Approach 1: Git Sync — Docs Change with Code

The most reliable way to keep docs current is to store them in the same repository as the code they describe, or in a connected repository that syncs bidirectionally.

When documentation lives in git, you can:

  • Require doc updates in PRs. Add a CI check or review rule: if code in /api/ changes, the PR must also touch files in /docs/api/. This doesn’t guarantee quality, but it guarantees someone at least looked at the relevant docs.

  • Use git log to find drift. Compare the last-modified dates of code files and their corresponding doc pages. If auth_handler.go was updated 3 months ago but docs/authentication.md hasn’t changed in 8 months, that’s a staleness signal.

  • Review docs in the same diff. When a developer changes how pagination works, the reviewer sees both the code change and the doc update in one pull request. Context is preserved. The reviewer can verify the docs match the implementation.

The challenge with git-based docs is that not everyone on a documentation team uses git. Technical writers, product managers, and support engineers often prefer a web editor. This is where bidirectional git sync matters — it lets web editors and git users work on the same content without either workflow breaking the other.

Valoryx’s Content Ledger pattern handles this by tracking every edit regardless of origin (web UI, git push, API, MCP tool) and reconciling changes automatically. See How Bidirectional Git Sync Works for the technical details.

Approach 2: MCP-Assisted Review — AI Flags Stale Content

Manual audits of documentation are thorough but rare. Nobody has time to read every page quarterly and check it against the current codebase. AI assistants with structured access to your docs can do this continuously.

With Model Context Protocol (MCP), you can connect an AI assistant to your documentation instance and run targeted audits. Here’s what this looks like in practice.

Finding Pages That Reference Deprecated Endpoints

Prompt: "Search all pages for references to /api/v1/ — we migrated
to /api/v2/ last month. List every page that still mentions v1."

The assistant calls the docplatform_search MCP tool, finds every occurrence, and returns a list of matching pages. What would take a human 30 minutes of grep-and-read takes the AI about 10 seconds.

Detecting Terminology Drift

Prompt: "Find all pages that use the term 'workspace group'. We
renamed this to 'organization' in v3.2. List them."

Same tool, different query. The assistant finds every instance of outdated terminology. You review the list and decide which pages to update.

Generating a Staleness Report

Prompt: "Which pages in the API Reference workspace show no recent
activity? List the quietest ones first."

The assistant pulls the recent-activity feed with docplatform_get_activity and cross-references it against the full page tree from docplatform_get_tree — pages with no recent edits are your staleness candidates.

Making the Updates

Once you’ve identified stale content, the AI can draft updates:

Prompt: "Read the current authentication guide. The login endpoint
now accepts passkeys in addition to passwords. Update the guide to
cover both methods, keeping the existing structure."

The assistant reads the page via docplatform_read_page, drafts the update, and applies it via docplatform_update_page. The edit shows up in the revision history and, if git sync is configured, appears as a commit you can review in a pull request.

The key insight is that MCP makes the AI a participant in your docs workflow, not a disconnected text generator. It reads your actual content, makes changes through the same system your team uses, and leaves an audit trail.

Approach 3: PR-Based Doc Reviews

The third approach is organizational: use the same review process for docs that you use for code.

The Doc Review PR

When a feature ships, create a follow-up task: “Update docs for feature X.” This task results in a pull request that:

  1. Updates the relevant documentation pages
  2. Gets reviewed by someone who understands the feature
  3. Runs through the same CI pipeline as code changes

This works because it’s not a new process. Your team already reviews PRs. Adding doc updates to the same workflow means they get the same attention.

Automated Staleness Checks in CI

You can automate staleness detection in your CI pipeline. A simple approach:

#!/bin/bash
# Find docs not updated in 90 days
STALE_THRESHOLD=$(date -d '90 days ago' +%s)

for doc in docs/**/*.md; do
  LAST_MODIFIED=$(git log -1 --format="%ct" -- "$doc")
  if [ "$LAST_MODIFIED" -lt "$STALE_THRESHOLD" ]; then
    echo "STALE: $doc (last updated $(git log -1 --format='%ci' -- "$doc"))"
  fi
done

Run this in CI weekly. If stale docs are found, create an issue automatically. This turns “docs might be stale” from a vague concern into a concrete, tracked task.

Review Rotation

Assign documentation review to a rotating schedule, the same way you assign on-call rotations. Each week, one person reviews 5-10 pages. Not the whole site — just a slice. Over a quarter, the entire documentation set gets reviewed.

This is unglamorous work. But it’s the only way to catch the kind of staleness that automated tools miss: pages that are technically accurate but poorly organized, examples that use deprecated patterns, guides that assume context the reader doesn’t have.

Combining All Three

These approaches work best together:

  • Git sync catches drift at the source — when code changes, docs are in the same PR
  • MCP-assisted review catches what git sync misses — terminology changes, deprecated references, stale pages
  • PR-based reviews catch what AI misses — structural problems, unclear writing, missing context

A practical weekly workflow:

  1. Monday: Run an MCP audit of the most critical documentation sections (API reference, getting started guide). Review and merge any updates.
  2. During the week: Require doc updates in code PRs that touch user-facing behavior.
  3. Friday: The rotation reviewer reads 5-10 pages, files issues for anything that needs attention.

This doesn’t require a dedicated documentation team. It distributes the work across people who already understand the content. The tooling — git sync, MCP, CI checks — reduces the effort per person to something sustainable.

Getting Started

If your docs are already stale, start with the highest-traffic pages. Documentation traffic is heavily skewed — a small fraction of pages gets most of the visits. Fix those first.

Then set up the infrastructure to prevent future staleness:

  1. Install Valoryx and connect your documentation to a git repository
  2. Configure MCP for your AI assistant so you can run audits on demand
  3. Add a staleness check to your CI pipeline
  4. Start a lightweight review rotation

Documentation goes stale because the feedback loop between code changes and doc updates is broken. Git sync tightens the loop at the source. MCP gives you a fast auditing tool. PR-based reviews add human judgment. Together, they make “keeping docs up to date” a tractable problem instead of a losing battle.