Introducing the AudioEye SDK MCP: Accessibility Expertise Inside Your AI Coding Agent

AI agents are writing more of the web every quarter. Research shows of more than 15,000 developers, nearly half of all code is now fully written by AI agents(opens in a new tab). Those agents also inherit every accessibility mistake in their training data and reproduce it at speed: the unlabeled button, the dropdown no keyboard can reach, the color contrast nobody checked. The defects have been around for years. What’s new is how fast AI repeats them.

AudioEye's SDK MCP fixes this knowledge gap by putting accessibility expertise directly into your coding process. It gives an AI coding agent direct access to AudioEye Intelligence, our proprietary accessibility data set, so issues are caught and corrected in the code the agent is already producing, and developers review and approve fixes rather than writing accessibility code by hand.

What is Model Context Protocol and Why Does it Matter for Accessibility?

Model Context Protocol (MCP) is an open standard that connects an AI agent to an external source of data and expertise. A developer's coding agent already reads and writes code; the MCP gives it a way to pull in data it wasn't trained on.

Accessibility is one of the clearest cases for that gap. AI coding agents learn from the public web, which is largely inaccessible, so they miss real accessibility issues and write confident but incorrect fixes. The AudioEye SDK MCP gives them a verified accessibility data set to work from instead.

What the AudioEye SDK MCP Does

The AudioEye SDK MCP runs AudioEye’s full detection ruleset, built against WCAG 2.2 Level AA. Independent testing found AudioEye’s automated detection identifies up to 2.5 times as many accessibility issues as other automated tools (Adience, January 2026). 

Every fix follows the same three-step loop, run inside the agent session:

• Scan the code: The agent scans the file or component in front of it and flags accessibility issues against AudioEye’s detection ruleset.

• Write the fix: The agent writes a fix for each flagged issue, generated from AudioEye Intelligence, rather than a generic pattern match.

• Verify: Developers then verify the same code after the fix and confirm the issue is gone before the code ships.

That ruleset is the core differentiator. Most accessibility tools run on a free, open-source rules engine. The AudioEye SDK MCP runs on AudioEye Intelligence, a proprietary dataset built on 14 years of accessibility work, more than 1 million human reviews, and data from more than 100,000 sites. The agent calling that data set is interchangeable. The data set is not. 

Every fix it produces still goes back to the developer for review and approval. Nothing reaches production without a person approving it. That review step matters because agents can confidently apply the wrong fix. That risk is why many engineering teams keep AI out of production codebases in the first place.

Tools the SDK MCP Exposes to your Agent

The AudioEye SDK MCP exposes two tool calls to a connected AI coding agent:

• A scan tool that returns accessibility issues found in the current code.

• A fix tool that generates a code-level correction for a flagged issue.

The agent calls these tools directly inside the same session where the developer is already working. There is no separate dashboard or scanner to open.

How to Connect to the AudioEye SDK MCP

Connecting the AudioEye SDK MCP takes an AudioEye SDK seat and an entry in your AI coding agent’s MCP client configuration. 

Full steps can be found in the AudioEye SDK MCP developer documentation.(opens in a new tab)

Accessibility symbol set inside a square box with various healthcare icons surrounding it against a dark purple background.

Where it Fits in Your Existing Workflow

The AudioEye SDK MCP runs upstream of the CI/CD pipeline, not as a gate inside one. Issues are caught and fixed while a developer is still writing code, so fewer reach a pull request in the first place. That's a different point in the workflow than a pipeline scan that blocks a build after the code is already written.

It's also only half of the coverage picture. Automation checks code for accessibility issues in the session where it's written; expert testing brings certified experts and assistive technology users to the judgment calls no ruleset can make, like a link coded correctly but does nothing when someone activates it. Run together, automation and expert fixes reach roughly 97% issue coverage, a level automation alone can't reach.

Accessibility that Keeps Pace with What You Ship

AI coding agents are already writing a growing share of production code, and that shift shows no sign of slowing. Accessibility work has to move at the same speed, or it keeps landing as a separate cleanup pass after the fact, rather than being built into the code in the first place. Closing that gap means putting accessibility knowledge where the agent already works, not in a separate tool it has to be told to check. 

This is exactly what the AudioEye SDK MCP is designed to do. It gives AI coding agents access to AudioEye Intelligence, a proprietary data set built over 14 years, within the tools developers already use. Issues get found and fixed in the same session the code is written, not weeks later in a separate audit. And a developer still approves every fix before it ships.

Ready to see how the AudioEye SDK MCP fits inside your engineering workflow? Talk to an expert today.