How to Use the Microsoft Clarity MCP in OpenAI Agents SDK

Q: Can my OpenAI Agents SDK setup run automated checks on rage clicks using Microsoft Clarity?

Yes. Your agent runs a cron loop calling listrageclicks to find frustrated users. It then pulls the specific session details using getrecording to diagnose the interface bug.

Q: How does the Microsoft Clarity MCP Server handle high traffic volumes in OpenAI Agents SDK?

It queries the API directly using tools like getdashboard. Your OpenAI Agents SDK handles the rate limits through its built-in execution guardrails.

Q: Can I filter heatmaps by page using this integration?

Yes. The agent calls listtoppages to find your highest-traffic URLs, then requests specific layout data via getheatmap.

Feed real-time user frustration data straight to your OpenAI Agents SDK pipelines to fix broken UI flows before your support queue blows up.

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Connect Microsoft Clarity MCP to OpenAI Agents SDK

Create your Vinkius account to connect Microsoft Clarity to OpenAI Agents SDK 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 Microsoft Clarity with OpenAI Agents SDK

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Isolate Broken UI Flows with OpenAI Agents SDK

The `list_rage_clicks` tool exposes raw frustration metrics directly to your Python-based agents. Your agent calls this endpoint to pinpoint exactly where users are slamming their mouse buttons in anger. Instead of guessing why a checkout form fails, the agent uses `get_recording` to pull the exact session logs. You build guardrails around these tools so your agent analyzes the friction without exposing sensitive customer data.

Automate UX Audits via Live Session Inspection

The `list_recordings` tool grabs the latest user sessions that ended in early exits. It filters out the noise and zeros in on the exact moments where user behavior deviates from the happy path. The agent calls `get_scroll_depth` to see if users even reach the primary call to action. It matches these scroll patterns with `list_dead_clicks` to build a prioritized list of layout bugs for your engineering team.

Track Live Site Health and Interaction Heatmaps

The `get_live_insights` tool pulls real-time user engagement metrics directly into your agent's decision loop. This lets your system flag sudden drops in user activity without waiting for daily reports. To confirm visual bugs, the agent invokes the MCP Server to call `get_heatmap` to inspect element interactions. It checks `check_clarity_status` first to ensure the tracking API is online before starting its diagnostic run.

Setup guide

Set up Microsoft Clarity MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Microsoft Clarity tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Microsoft Clarity tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Microsoft Clarity tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Microsoft Clarity Agent",
            instructions="You have access to Microsoft Clarity tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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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 Microsoft Clarity. 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 Microsoft Clarity MCP in OpenAI Agents SDK

Install the package, initialize the HTTP server transport, and pass the server to your agent constructor. The agent auto-discovers tools like `list_projects` without manual schema registration.

Yes. Your agent runs a cron loop calling `list_rage_clicks` to find frustrated users. It then pulls the specific session details using `get_recording` to diagnose the interface bug.

It queries the API directly using tools like `get_dashboard`. Your OpenAI Agents SDK handles the rate limits through its built-in execution guardrails.

Yes. The agent calls `list_top_pages` to find your highest-traffic URLs, then requests specific layout data via `get_heatmap`.

No. The server only returns anonymized interaction events, scroll percentages, and click coordinates. Your raw session recordings and keystrokes are masked at the source before the agent ever reads them.

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