How to Use the Microsoft Clarity MCP in LangChain

Q: How do I pass Microsoft Clarity rage click data to a LangChain agent?

You register the listrageclicks tool with your LangChain agent using the MCP adapter. The agent calls this tool during its reasoning loop to fetch users who are actively struggling on your site.

Q: Can LangChain run multi-step audits using the Microsoft Clarity MCP Server?

Yes, you can chain listdeadclicks with getrecording to build a diagnostic pipeline. The agent finds the dead clicks first, then pulls the exact recordings to explain the friction.

Q: How does LangSmith help trace Microsoft Clarity tool calls?

Every time your agent invokes getheatmap or getdashboard, LangSmith records the latency and payload. You see exactly what behavioral data the model used to make its layout decisions.

Feed raw behavioral analytics straight into your LangChain runs to fix broken UI patterns on the fly.

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Connect Microsoft Clarity MCP to LangChain

Create your Vinkius account to connect Microsoft Clarity to LangChain 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 LangChain

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Claude

Perplexity

Pinpoint user frustration points using LangChain chains

The `list_rage_clicks` tool pulls real-time frustration metrics directly into your agent's execution loop. Your chain can immediately flag pages where users are hammering their mouse buttons out of anger. LangChain coordinates this behavioral data with your backend tools. If rage clicks spike, the agent triggers an alert with the exact session context.

Audit user paths with this MCP Server

The `get_recording` tool retrieves specific session playback details so your LangChain agent can analyze where users get stuck. This isn't generic telemetry; it's step-by-step user behavior. You feed these logs into a debugging chain to isolate UI bugs. LangSmith traces the entire flow, showing you exactly how the agent processed the recording data.

Map scroll behavior in multi-step pipelines

The `get_scroll_depth` tool measures how far down a page users actually read. Your LangChain agent calls this tool to decide if a layout needs a shorter design. By chaining this with `list_top_pages`, the agent evaluates content placement across your most visited URLs. You get clear, data-backed layout suggestions without reading manual reports.

Setup guide

Set up Microsoft Clarity 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 Microsoft Clarity 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({
    "microsoft-clarity-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 Microsoft Clarity 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 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 LangChain

You register the `list_rage_clicks` tool with your LangChain agent using the MCP adapter. The agent calls this tool during its reasoning loop to fetch users who are actively struggling on your site.

Yes, you can chain `list_dead_clicks` with `get_recording` to build a diagnostic pipeline. The agent finds the dead clicks first, then pulls the exact recordings to explain the friction.

Every time your agent invokes `get_heatmap` or `get_dashboard`, LangSmith records the latency and payload. You see exactly what behavioral data the model used to make its layout decisions.

Yes, you can use the multi-server client to merge these behavioral tools with database tools. This lets your agent pull a recording and immediately write a bug ticket in your tracker.

Absolutely. Your session recordings and dead click logs never touch persistent storage on our end. Vinkius runs this MCP Server in an ephemeral V8 sandbox, passing the data directly to your local LangChain execution environment.

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