How to Use the Microsoft Clarity MCP in LlamaIndex

Q: How do I index Microsoft Clarity session logs into LlamaIndex?

Use the MCP tool spec to pull data from listrecordings into your ingestion pipeline. LlamaIndex converts the session metadata into document nodes for semantic search.

Q: Can LlamaIndex query Microsoft Clarity heatmaps semantically?

Yes, by calling getheatmap through the agent, LlamaIndex parses the coordinates and click counts. The agent then indexes this data so you can ask plain-English questions about where users click.

Index live Microsoft Clarity behavioral data directly into LlamaIndex vector stores for semantic search.

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Connect Microsoft Clarity MCP to LlamaIndex

Create your Vinkius account to connect Microsoft Clarity to LlamaIndex 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 LlamaIndex

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Perplexity

Index user frustration for LlamaIndex RAG

The `list_dead_clicks` tool extracts instances where users clicked static elements expecting an action. Your LlamaIndex pipeline indexes these events into a vector database for semantic querying. When you query your knowledge base about UI bugs, the agent retrieves these actual friction events. You get answers grounded in real user behavior, not guesses.

Query visual behavior with this MCP Server

The `get_heatmap` tool provides the click and scroll distribution data for any tracked page. LlamaIndex ingests this spatial data to build a semantic map of user attention. Your agent searches this index to find which sections of your homepage get ignored. This turns raw visual telemetry into queryable knowledge.

Retrieve live metrics for context-rich queries

The `get_dashboard` tool pulls current session counts, bounce rates, and device distributions. LlamaIndex uses this tool to inject live site health metrics directly into your system prompts. This prevents your agent from relying on stale data when analyzing conversion drops. You get real-time context parsed and indexed instantly.

Setup guide

Set up Microsoft Clarity MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Microsoft Clarity MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Microsoft Clarity tools.",
)
response = await agent.run("List recent Microsoft Clarity data")

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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 LlamaIndex

Use the MCP tool spec to pull data from `list_recordings` into your ingestion pipeline. LlamaIndex converts the session metadata into document nodes for semantic search.

Yes, by calling `get_heatmap` through the agent, LlamaIndex parses the coordinates and click counts. The agent then indexes this data so you can ask plain-English questions about where users click.

You can use the allowed tools filter when setting up your tool list. This lets you restrict your agent to specific tools like `list_rage_clicks` while hiding administrative project tools.

Yes, you can cache retrieved behavioral metrics in your local vector store. This lets you run offline evaluations on user friction patterns without hitting API limits.

All behavioral telemetry, including scroll depths and click coordinates, is retrieved over a secure MCP connection. The server runs inside an isolated, zero-trust container that destroys all session data immediately after transmission.

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