How to Use the Umami Cloud MCP in LlamaIndex

Index Umami Cloud analytics results with LlamaIndex to build a searchable knowledge base.

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Connect Umami Cloud MCP to LlamaIndex

Create your Vinkius account to connect Umami Cloud 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 Cloud with LlamaIndex

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Search user counts using `users`

The `users` tool gives your agent the total active user count for a site. You run this call, and LlamaIndex indexes that specific number into your vector store. Later, you can query past session data—like 'What was the max user count last Tuesday?'—and get an answer grounded in the actual API results.

Build knowledge from `websites` metrics

Using the `websites` tool collects deep visitor data: URLs, browsers, OS types, and devices. LlamaIndex turns these structured reports into searchable chunks of knowledge. You don't just run the query; you index it. You can then ask questions like 'Which common browser caused a spike in visits last month?' and get precise answers.

Query past analytics data

You combine both tools into one RAG application. First, the agent retrieves user counts via `users`, indexing that value. Then it runs `websites` to gather detailed metrics. The resulting index allows you to ask complex questions like 'Show me all device types associated with high-volume traffic reported by Umami Cloud last quarter.'

Setup guide

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

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Common questions about Umami Cloud MCP in LlamaIndex

LlamaIndex takes the raw outputs from Umami Cloud's tools, like `users` or `websites`, and turns them into indexed knowledge. This lets you query API results rather than just asking general questions.

Yes. The server's tools are perfect for indexing. You can store specific metrics—like the count of active users or list of visited URLs—and query them later using semantic search.

The server offers two key data sets: a simple active user count via `users`, and rich visitor details covering browsers, OS, URLs, and devices using the `websites` tool.

Because LlamaIndex indexes results, you can query past data. You index a report from yesterday's run of `websites`, and then ask questions about it today.

It handles web traffic metrics, specifically active user counts, URLs, browser information, operating systems, and device details.

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