How to Use the Accessibility Checker MCP in LangChain

Build complex, multi-step reasoning pipelines using LangChain's advanced agents.

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Connect Accessibility Checker MCP to LangChain

Create your Vinkius account to connect Accessibility Checker to LangChain — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Accessibility Checker with LangChain

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Assess entire pathways for compliance.

The `check_corridor_and_passage` tool confirms if a specific pathway meets minimum clear width standards. You can chain this result with other checks to build a full path assessment, ensuring every segment is compliant. Don't just check one spot; run an entire sequence of tests. Your agent handles the decision-making: first checking `check_doorway` clearance, then running `check_ramp_compliance`, and finally confirming overall flow.

Verify functional space in restrooms.

Need to confirm a bathroom works for mobility aids? Use `check_bathroom_spaciousness`. This tool validates if the room offers enough maneuverable space, which is crucial for full accessibility. After confirming the right size, you can pass those measurements into other nodes in your chain. For example, you might use the dimensions confirmed by this tool to inform a subsequent call to `check_doorway`.

Confirm ramp slopes and landings.

The `check_ramp_compliance` function determines if a ramp section adheres to maximum slope limits and requires proper transition landing areas. It's the safety net for vertical movement. You can make your agent run this check, get the result, and then use that pass/fail status as an input variable for another MCP call in the chain. This builds airtight reasoning.

Setup guide

Set up Accessibility Checker MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Accessibility Checker tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "accessibility-checker-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Accessibility Checker transactions"
    })
    print(result["messages"][-1].content)

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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 Accessibility Checker. 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 Accessibility Checker MCP in LangChain

You feed our Accessibility Checker tools into your multi-step agent structure. The power is that the output of one tool—say, a passage width failure—becomes the explicit input for the next tool to address.

Absolutely. You can chain together all four core functions: corridor passages, doorways, bathrooms, and ramps. Your agent decides the optimal order for calling them based on your initial prompt.

It provides structured compliance status and detailed violation reports (e.g., 'Passage width is 120cm, minimum required is 150cm'). This clean, actionable JSON output makes it perfect for agent decision-making.

Yes. Since you're building chains, you aren't limited to single checks. You can build a workflow that first assesses the doorway and then automatically runs ramp compliance if the door is compliant.

It handles structural measurements, specifically clear opening widths for doorways and minimum passage dimensions. This data dictates overall building code adherence.

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