How to Use the Umami (Privacy Analytics) MCP in LlamaIndex

Q: Can I query past analytics data using Umami (Privacy Analytics) and LlamaIndex?

Yes, that's the point. By indexing results from getwebsiteeventdata, your agent can pull specific answers about historical user behavior without needing complex filters.

Q: Does Umami (Privacy Analytics) handle sensitive data types with LlamaIndex?

The server handles event property and value counts. Indexing these specific fields allows you to analyze trends without exposing raw, personally identifiable information.

Q: What exact data type does Umami (Privacy Analytics) touch? (LlamaIndex)

This MCP Server handles event names and counts, giving you precise knowledge of what actions users performed on the website.

Ground complex analytics queries against historical data with Umami (Privacy Analytics) and LlamaIndex.

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Connect Umami (Privacy Analytics) MCP to LlamaIndex

Create your Vinkius account to connect Umami (Privacy Analytics) 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 Umami (Privacy Analytics) with LlamaIndex

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Perplexity

Indexing deep event activity for semantic search.

Instead of just getting a list, you index the actual raw details. Call `get_website_event_data` and feed those results into your vector store. You can then query past events—like finding all instances where 'checkout' failed—and get answers grounded in real API data.

Retrieving historical user session context.

Need to know what a specific user did three months ago? Indexing `get_website_sessions` makes that possible. Your RAG application pulls the full history, letting you answer complex questions like, 'What was the typical path for users who viewed Product X?'

Building an administrative knowledge base using MCP Server.

Don't rely on memory. Indexing configurations allows you to query about setup details. You can ask the system, 'What are the metrics available for this website?' and it pulls detailed results from `get_website_metrics` directly into your searchable index.

Setup guide

Set up Umami (Privacy Analytics) 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 Umami (Privacy Analytics) 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 Umami (Privacy Analytics) tools.",
)
response = await agent.run("List recent Umami (Privacy Analytics) data")

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Common questions about Umami (Privacy Analytics) MCP in LlamaIndex

LlamaIndex takes the structured output from MCP tools, like `create_revenue_report`, and turns it into indexed chunks. This allows your agent to perform semantic searches across massive datasets.

Yes, that's the point. By indexing results from `get_website_event_data`, your agent can pull specific answers about historical user behavior without needing complex filters.

You can index anything that describes the structure: team names, website properties (`get_website`), or user roles. This makes your entire analytics platform searchable.

The server handles `event property and value counts`. Indexing these specific fields allows you to analyze trends without exposing raw, personally identifiable information.

This MCP Server handles `event names and counts`, giving you precise knowledge of what actions users performed on the website.

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