Accessibility MCP Servers: What They Are and How They Work

Author: Level Access

An accessibility Model Context Protocol (MCP) server connects AI assistants to testing engines, Web Content Accessibility Guidelines (WCAG) guidance, or existing findings, so developers can fix issues in their editor.

You already ask an AI assistant to write components, review pull requests, and explain code. Accessibility testing usually happens somewhere else: in a separate tool or a later pipeline stage. Every jump between tools slows delivery, and accessibility issues found late become release blockers.

An accessibility MCP server closes that gap by bringing accessibility testing and data into AI-assisted development. This guide for developers and engineering leads explains how these servers work, the four kinds, setup, and example prompts. It also covers their limits.

Key insights

• The Model Context Protocol (MCP) is an open standard for connecting AI assistants to external tools and data. It’s now governed under the Linux Foundation.
• An accessibility MCP server brings accessibility testing engines, WCAG guidance, or existing findings into your AI assistant. The assistant can check, suggest a fix, and re-check in one loop.
• There are four kinds of accessibility MCP servers: testing-engine, browser-automation, WCAG reference, and platform-data. A scanner tells the assistant what it can detect right now. A platform-data server tells it what your team, including human experts, has already found.
• Setup depends on whether a server runs locally or remotely. The best prompts ask for specific, checkable work and the WCAG criterion tied to every finding.
• An MCP server can’t confirm WCAG conformance on its own. Pair it with least-privilege security controls, continuous integration (CI) checks, and manual evaluation.

What is the Model Context Protocol?

The Model Context Protocol (MCP) is an open standard for connecting AI applications to external tools and data. Anthropic introduced it in November 2024, then donated it to the Linux Foundation’s Agentic AI Foundation in December 2025. Anthropic’s announcement cited more than 10,000 active public MCP servers at the time.

The MCP architecture documentation describes three parts:

• Host: The AI application you work in, such as Claude Desktop, Claude Code, or VS Code.
• Client: A connector the host creates for each MCP server it uses.
• Server: A program that exposes tools for actions, resources for data, and prompts for templates.

When the model needs a capability, it makes a tool call with a tool name and arguments, and the server returns a structured result. In short, MCP standardizes how AI assistants reach systems outside the model, from file systems to accessibility testing engines. Local servers run on your machine over standard input and output (stdio). Remote servers run as hosted services over HTTP.

What is an accessibility MCP server?

An accessibility MCP server is a Model Context Protocol server that gives an AI assistant access to accessibility testing engines, WCAG guidance, or existing findings. Developers can then find and fix accessibility issues without leaving their editor. You’ll also find these called a11y MCP servers or MCP servers for accessibility testing.

When building a form in your editor, with an accessibility MCP server, you can ask the assistant to test it, explain each finding against WCAG, and propose a fix in the same session.

Four kinds of accessibility MCP servers

Accessibility MCP servers fall into four main categories. Testing-engine servers find new issues on demand. Browser-automation servers let AI assistants drive real browsers. WCAG reference servers give AI assistants WCAG guidance. And platform-data servers bring issues your testing program has already found, including human-validated ones, into the assistant.

Testing-engine servers

Testing-engine servers wrap accessibility testing APIs in tools the assistant can call. Most use axe-core and drive a headless browser through Puppeteer or Playwright. They test pages at a public URL or on localhost. Many can also test raw HTML strings before a component ships.

Accessibility violations come back with rule IDs, the WCAG criterion and level, the violating element, and remediation guidance. Servers using axe-core typically support WCAG 2.0, 2.1, and 2.2 at Levels A, AA, and AAA. Common axe-core accessibility rules cover color contrast, image alt text, form labels, duplicate IDs, and many check that ARIA attributes are valid and used correctly. The limit: These scans find only what automated rules can detect.

How a testing-engine server works, step by step

A testing-engine server typically follows this loop:

1. You make a request, such as testing your checkout page for WCAG 2.2 Level A and AA issues.
2. The assistant makes a tool call, passing a URL or a block of HTML.
3. The server returns structured results: rule ID, WCAG success criterion, the element identified by CSS selectors, and severity.
4. The assistant explains each issue in plain language and suggests a code change.
5. The assistant re-checks to confirm the fix worked and didn’t create new accessibility problems.

Because the loop runs in your editor, you catch issues while you’re still working on the code. Other kinds of servers change what goes into the loop. A platform-data server, for example, starts it from issues your team has already found.

Browser-automation servers and the accessibility tree

Browser-automation servers let an AI assistant drive a real browser: Open a page, click, type, and track what happens. They’re helpful for checking keyboard navigation, focus order, and dynamic content.

Microsoft’s Playwright MCP server gives the assistant accessibility snapshots by default, not a full page screenshot. The assistant works with the same names, roles, and states that assistive technologies use, and it reaches interactive elements through that structure. You can use this server to check where focus lands after a dialog opens or closes, or reproduce an issue in a specific browser context.

This has a side effect worth knowing: AI agents that read the accessibility tree work better on accessible pages. Missing labels and roles confuse agents the same way they confuse screen reader users. Still, the assistant has to interpret what it finds, so this isn’t an accessibility test by itself.

WCAG reference servers

WCAG reference servers give AI assistants structured access to WCAG 2.2 success criteria, techniques, and glossary terms. LLMs can misremember details of the WCAG guidelines, such as a criterion’s exceptions or level. A reference server grounds answers in the actual text of WCAG 2.2, which helps when you need to check whether an exception applies or what a criterion covers. However, the server only knows the WCAG standards, not your product.

Platform-data servers

Platform-data servers bring accessibility data your organization already has into an AI assistant, including automated scan results and findings from manual review.

For example, the Level Access MCP server lets an assistant:

• Pull open issues for a site, app, or flow into a fix session, with full details on each one. That matters because the hardest issues to find, like confusing screen reader output or a broken keyboard flow, usually come from human testers, not scanners.
• Find recent automated scan results and filter them by severity, standard, or status, including results from multiple testing engines.
• Bring in findings from manual evaluations, so human-validated results sit alongside automated ones.
• Track progress over time with metrics for an asset, a team, or the whole organization.

Setup requires an API token. Like any platform-data server, it’s only as complete as the testing program behind it.

How to choose an accessibility MCP server

Start with the question you need your assistant to answer:

• “Does the code I just wrote have detectable accessibility issues?” Use a testing-engine server.
• “Can a keyboard user get through this flow?” Use a browser-automation server, then confirm the results with manual testing.
• “What does this WCAG criterion actually require?” Use a WCAG reference server.
• “What should I fix first, based on what we already know?” Use a platform-data server.

You can also combine them. For example, pair a testing-engine server for new code with a platform-data server for known issues. The assistant can then check new work and fix existing problems in the same session.

Setting up an accessibility MCP server

Testing-engine and browser-automation servers usually run locally. You install them through npm or Docker, and your AI client starts them with a command. Platform-data servers usually run remotely with an API token. A local configuration typically follows this shape:

{
  "mcpServers": {
    "accessibility": {
      "command": "npx",
      "args": ["-y", "<accessibility-mcp-package>"]
    }
  }
}

Remote servers use a URL and an authentication header instead of a local command. The exact format depends on the AI client:

• Claude Desktop stores MCP servers in its own JSON configuration file.
• Claude Code adds servers with claude mcp add and can share them through a project .mcp.json file.
• VS Code reads an mcp.json file from your workspace or user profile, under a servers key.

Check each server’s documentation for configuration options. As a best practice, store API tokens in an environment variable, not a committed file. For pages behind a login form, use a test account rather than a personal session cookie.

Security and data questions to ask first

The MCP security best practices and OWASP guidance stress least privilege and caution with untrusted servers. Before you connect one, ask:

• Credentials: Are tokens scoped to least privilege and kept out of committed files?
• Data flow: What data can the assistant reach through the server, and what reaches the model provider?
• Authorization: Is it clear who approved the connection and what the assistant can do?
• Untrusted servers: Did you install it from a verifiable source and review the MCP tools it exposes?
• Prompt injection: Content from scanned web pages returns to the model as tool output. Treat it as untrusted input.
• Audit trail: Can you find which tools the agent called, and when?

Example prompts for an accessibility MCP server

The most useful prompts ask for specific, checkable accessibility testing work.

Testing-engine server prompts

• “Run an accessibility check on http://localhost:3000/checkout and list WCAG 2.2 Level A and AA violations by severity.”
• “Check this component’s HTML for accessibility issues, suggest fixes, then re-check after I apply them.”
• “Check the login form at a mobile viewport width of 375 pixels for insufficient color contrast.”

Browser-automation server prompts

• “Open the sign-up page, move through it using only the Tab key, and tell me where focus goes and anything I can’t reach.”

Reference server prompts

• “What does WCAG 2.2 Success Criterion 2.5.8 Target Size (Minimum) require, and what are its exceptions?”

Platform-data server prompts

• “List the highest-severity open accessibility issues for our checkout flow and group them by component.”
• “Summarize the most recent manual evaluation of our mobile app and draft ticket text for the top five findings.”
• “How has the number of critical accessibility issues across our workspace changed over the last 90 days?”

Three habits make any prompt more useful:

• Ask for the WCAG criterion and level on every finding.
• Ask the assistant to re-check after each fix.
• Never ask it to “make the site compliant.” No automated check can confirm conformance with accessibility standards or other legal requirements.

What an accessibility MCP server can’t do

An MCP server speeds up finding and fixing accessibility issues, but a clean result isn’t proof that an experience conforms with standards—or, more importantly, works for real users with disabilities. Here are a few specific limitations to be mindful of:

• Validating conformance: Automated engines detect only machine-checkable issues. As WebAIM notes, finding no errors doesn’t mean a page is accessible or conformant.
• Recommending foolproof fixes: Suggested fixes can be wrong or add unnecessary ARIA.
• Understanding real user experience: A server can’t operate assistive technologies like a screen reader, judge whether content makes sense in context, or experience a flow the way a real user would. That takes manual testing by accessibility experts and users with disabilities.
• Testing across multiple states and viewports: Results reflect one state or viewport at a time. A scan at desktop viewport width won’t cover a mobile viewport, and some servers can’t reach content inside a shadow root. Two scans of the same page may not return the same results if content loads after the scan runs.
• Knowing what’s already been found: A testing-engine server starts from zero every time. Unless it’s connected to your testing history, the assistant can’t access issues found in past audits or know which ones your team has prioritized.

How MCP servers fit with CI checks and manual testing

Because accessibility MCP servers have limits, they’re best used alongside other testing methods; specifically, CI checks and manual audits. Here’s how these three approaches work together:

• MCP servers bring web accessibility testing into coding, when a developer asks.
• CI checks catch regressions. For example, tools like Level CI run automated tests on every change in a continuous integration and continuous delivery (CI/CD) pipeline and deliver results, along with recommended fixes, in pull requests. Level CI also has its own MCP, so developers can access CI results in their IDE.
• Manual evaluation validates conformance. Experts test key pages, components, and user flows with assistive technologies to identify issues that automation can’t. User testing by people with disabilities goes beyond traditional audits to help you understand whether an experience works as intended for real users.

As a best practice, layer all three, since no single method gives comprehensive coverage of web accessibility.

Bring accessibility context into your coding assistant

More code than ever is being written and reviewed by AI assistants. For that code to be accessible, assistants need the right information and resources.

The Level Access MCP server connects AI assistants to automated and manual findings in the Level Access Platform, bringing accessibility data and expertise into AI-enabled development workflows. Explore our full range of developer tools—including SDKs, CI/CD tools, and a public API—or request a demo to learn how our solution can power smarter accessibility for your team.