How to Use the WCAG.com Accessibility MCP in LlamaIndex

Q: Can LlamaIndex store WCAG.com Accessibility audit results?

Absolutely. The output from tools like auditurlaccessibility becomes part of the searchable knowledge base. You can query historical compliance data later on.

Q: Does WCAG.com Accessibility require specific setup for LlamaIndex?

You use pip install llama-index-tools-mcp and initialize the client using BasicMCPClient. This allows you to integrate the MCP Server's tools directly into your RAG pipeline.

Index WCAG audit results into a searchable knowledge base using LlamaIndex.

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Connect WCAG.com Accessibility MCP to LlamaIndex

Create your Vinkius account to connect WCAG.com Accessibility to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup WCAG.com Accessibility with LlamaIndex

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Creating Searchable Audit Knowledge with MCP Server

When you run `audit_url_accessibility`, the tool returns structured compliance data. With LlamaIndex, this output doesn't just vanish; it gets indexed into your vector store. You can then query past audit sessions or configurations and get answers grounded in actual API data, not hallucinations.

Building RAG for WCAG Guidelines

Need to know the exact recommendation for a specific guideline? Call `get_wcag_guideline_details`. You can index these detailed metadata records, building a knowledge base around accessibility rules. This allows your application to answer questions like, 'What are the recommendations for Guideline X?' by querying the indexed data.

Monitoring WCAG Service Status

Before running any audit, you can use `check_api_status` to confirm the service is operational. This result—the status confirmation—is a perfect piece of knowledge to index. You combine this live API data with historical audit reports, making your entire RAG application much more reliable.

Setup guide

Set up WCAG.com Accessibility MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all WCAG.com Accessibility MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to WCAG.com Accessibility tools.",
)
response = await agent.run("List recent WCAG.com Accessibility data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by WCAG.com. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about WCAG.com Accessibility MCP in LlamaIndex

You pass the MCP Server's tools to the FunctionAgent after defining them with `McpToolSpec`. This lets you treat accessibility checks, like running audits or listing rules, as sources of knowledge for your index.

Absolutely. The output from tools like `audit_url_accessibility` becomes part of the searchable knowledge base. You can query historical compliance data later on.

LlamaIndex takes structured outputs—such as guideline details from `get_wcag_guideline_details` or lists of rules—and converts them into semantic vectors, making the data retrievable by meaning.

The server touches URLs for auditing and metadata/rules guidelines. All of this structured information is ingested into your unified, queryable index.

You use `pip install llama-index-tools-mcp` and initialize the client using `BasicMCPClient`. This allows you to integrate the MCP Server's tools directly into your RAG pipeline.

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