How to Use the UI/UX Excellence Prover MCP in AutoGen

Running multi-agent consensus with AutoGen and MCP Server validation.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect UI/UX Excellence Prover MCP to AutoGen

Create your Vinkius account to connect UI/UX Excellence Prover to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup UI/UX Excellence Prover with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

MCP Server for Debate Consensus

When multiple agents debate a design choice, the `validate_uiux_excellence` tool acts as the unbiased arbiter. It forces consensus on standards like semantic depth and proper spacing, rejecting any proposed layout that falls into 'Flat Decoration.' The system can use this MCP Server to challenge an agent's output: Is this button *really* important? The server confirms if its visual weight is appropriate for the task at hand.

Audit Agent Proposals

You can run competing designs from different AutoGen agents through `validate_uiux_excellence`. This tool checks which design best adheres to WCAG 2.2 AAA, specifically flagging if any agent introduced a 'Dark Pattern' like pre-checked boxes. It ensures that the final decision reached by the group is ethically and functionally sound for the end user.

Verify Motion Consistency

If your multi-agent system handles complex state transitions, this tool validates motion. It demands every animation explain or confirm something, preventing agents from settling on designs with 'Gratuitous Motion.' It enforces 60fps and spring physics, ensuring the user experience feels natural, not like a bad PowerPoint presentation.

Setup guide

Set up UI/UX Excellence Prover 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 UI/UX Excellence Prover 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="UI/UX Excellence Prover_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent UI/UX Excellence Prover data")
print(result.messages[-1].content)

Get your connection token →

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="UI/UX Excellence Prover_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent UI/UX Excellence Prover 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 UI/UX Excellence Prover. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect UI/UX Excellence Prover now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about UI/UX Excellence Prover MCP in AutoGen

It provides a concrete, objective standard for consensus. When agents argue about design, this MCP Server forces them to reconcile their ideas against technical best practices like the 8-state interaction model.

Yes. It operates on the raw component structure, making it useful whether your agents are working in a terminal environment or generating rich front-end code snippets.

It's better because it executes the rules. Instead of just saying 'use adequate contrast,' this MCP Server checks if the ratio is at least 4.5:1, providing a measurable pass/fail result.

This server touches UI/UX design structure and metadata. It analyzes the architectural requirements of the interface being debated, not user inputs or conversational history.

It ensures that every interactive element proposed by the agents has a complete lifecycle documented—including default, hover, focus-visible, and active states—preventing ambiguous user experiences.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript