How to Use the Accessibility Checker MCP in AutoGen

Q: Can I make my agents compare different types of checks using AutoGen?

Yes. You can force multiple, disparate tools—like checkdoorway and checkrampcompliance—to run simultaneously and have the agents argue about which failure is more critical to fix first.

Force consensus on compliance findings with AutoGen’s multi-agent debate framework.

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Connect Accessibility Checker MCP to AutoGen

Create your Vinkius account to connect Accessibility Checker to AutoGen — 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 AutoGen

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Debate pathway suitability.

Start by running `check_corridor_and_passage` to get a baseline report. Then, let your agents argue: one agent might focus on legal minimums while another checks against optimal usability standards. The MCP Server provides the raw data—the violation details and measurements—which the competing agents then debate until they reach a consensus recommendation.

Challenge bathroom sizing requirements.

Use `check_bathroom_spaciousness` as a foundational fact. You can set up two agents to challenge each other: one citing building code, and the other pointing out potential issues with specific mobility equipment sizes. The outcome isn't just 'compliant'; it's a negotiated decision on whether the space is functionally adequate.

Negotiate ramp requirements.

The `check_ramp_compliance` tool provides the core structural data (slope, landing length). Your agents then debate how that data interacts with other systems. For instance, one agent might flag a conflict between the slope and local electrical conduit placement. This is about finding the best possible solution when requirements clash.

Setup guide

Set up Accessibility Checker MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Accessibility Checker tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Accessibility Checker_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Accessibility Checker data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Accessibility Checker_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Accessibility Checker data")
    print(result.messages[-1].content)

Get your connection token →

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 AutoGen

Our MCP Server provides the objective, measurable data (like precise widths or slopes). Your agents use that data as undeniable facts to debate and reach a consensus decision on compliance.

Yes. You can force multiple, disparate tools—like `check_doorway` and `check_ramp_compliance`—to run simultaneously and have the agents argue about which failure is more critical to fix first.

The process shifts from simple tool execution to deliberative reasoning. The AI doesn't just report 'pass' or 'fail'; the agents debate *why* and what the implications are.

It provides measurable, verifiable facts: specific dimensions, required clear openings, and maximum allowed slopes. These concrete numbers fuel the agents' debates.

The core data types are structural measurements and dimensional requirements for passages, ramps, and entry points, allowing the agents to argue over physical limitations.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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