The full upstream README, mirrored here for reference. Install config, tool schemas, adoption signals, and an original overview live on the Accessibility Scanner MCP listing page.
An MCP server that lets an AI agent scan a web page for WCAG accessibility issues and get back findings it can act on. The agent calls one tool with a URL; it gets every violation grouped by severity, each with the exact element selector, the offending HTML, the specific failure, the WCAG success criterion, and a fix-guide link — plus the items that still need human review.
It runs the real axe-core engine in your local Chrome
(via playwright-core), so nothing about the pages you scan leaves your machine. It scrolls the page
first so lazy-loaded content is actually checked rather than silently skipped, and it resolves
color contrast over CSS gradients, which most tools leave as "needs review."
Upgrading from 0.1.x? 0.2.0 sees more of each page and returns more findings for the same URL. See CHANGELOG.md.
Part of accessibilityscanner.app.
CHROME_PATH environment variable to a Chromium binary)Add it to your MCP client's config. No global install needed — npx fetches it on first run.
Claude Desktop (claude_desktop_config.json), Cursor, Claude Code, or any MCP client:
If Chrome is not auto-detected, add an env block:
scan_accessibility| Input | |
|---|---|
url (string, required) | The http(s) URL to scan. |
Returns a report grouped by severity. For each rule: the WCAG criterion, a fix-guide link, and per element the selector, HTML, and exact failure. Example flow with an agent:
You: Audit https://example.com for accessibility and fix what you can. Agent: (calls
scan_accessibility) → reads the findings → edits the code → re-scans.
Automated testing covers the machine-checkable subset of WCAG (most of the issues on a typical page, but not all of it). Items that need human judgement are returned under "Needs manual review." It never claims a page is "compliant."
MIT