MCP server for accessible agentic coding — WCAG audit tools for AI coding agents. Built on @accesslint/core. From AccessLint.
Add to your MCP client configuration:
{
"mcpServers": {
"accesslint": {
"command": "npx",
"args": ["@accesslint/mcp"]
}
}
}- audit_html — Audit an HTML string for WCAG violations. Auto-detects fragments vs full documents.
- audit_file — Read an HTML file from disk and audit it.
- audit_url — Fetch a URL and audit the returned HTML.
- diff_html — Audit new HTML and diff against a previously named audit to verify fixes.
- list_rules — List available WCAG rules with optional filters by category, level, fixability, or criterion.
All audit and diff tools accept an optional
min_impactparameter to filter results by severity. Valid values, from most to least severe:critical,serious,moderate,minor. When set, only violations at that level or above are shown.
Each violation in the audit output includes the rule ID, CSS selector, failing HTML, impact level, and — where available — a concrete fix suggestion, fixability rating, and guidance. When multiple elements break the same rule, shared metadata is printed once to keep output compact.
To audit React components (.jsx/.tsx), the agent uses the audit-react-component prompt, which guides it through:
- Reading the component source
- Mentally rendering it to static HTML (acting as
renderToStaticMarkup) - Passing the rendered HTML to
audit_htmlwithcomponent_mode: true
No extra runtime dependencies are required — the agent renders the component itself based on the source code.
Without tools, the agent reasons about WCAG rules from memory. The MCP replaces that with structured output — specific rule IDs, CSS selectors, and fix suggestions — so the agent skips straight to applying fixes. This means 23% fewer output tokens per run, which translates directly to faster and cheaper completions.
Benchmarked across 25 test cases, 67 fixable violations, 3 runs each (Claude Opus):
| With @accesslint/mcp | Agent alone | |
|---|---|---|
| Violations fixed | 99.5% (200/201) | 93.5% (188/201) |
| Regressions | 1.7 / run | 2.0 / run |
| Cost | $0.56 / run | $0.62 / run |
| Duration | 270s / run | 377s / run |
| Timeouts | 0 / 63 tasks | 2 / 63 tasks |
MIT