How to Use the Umami Cloud MCP in Mastra AI

Q: Can I run a multi-step analysis using Umami Cloud with Mastra AI?

Yes. You can set up conditional branching: if users is below X, then execute a detailed check using websites. This makes the whole process very reliable.

Build resilient, multi-step web analytics workflows with Mastra AI.

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Connect Umami Cloud MCP to Mastra AI

Create your Vinkius account to connect Umami Cloud to Mastra AI 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 Mastra AI

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Perplexity

Complex Data Retrieval via MCP Server

The `websites` tool pulls detailed metrics—including URLs and browser types—for a specific site. You can build multi-step flows where one step checks the data, and the next acts on it.

Handling User Counts with Mastra AI

Need to check active users? Use the `users` tool to get that count. Because this is a workflow engine, you can wrap this call in retry logic if the connection hiccups during your process.

Multi-Step Analytics Workflows with Mastra AI

You're building complex operations here. You might first check `users` for a baseline count, then use that number to trigger a more detailed call to `websites`. The framework handles the whole sequence.

Setup guide

Set up Umami Cloud MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Umami Cloud tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "umami-cloud-mcp-client",
  servers: {
    "umami-cloud-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Umami Cloud Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Umami Cloud tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Umami Cloud transactions"
);
console.log(result.text);

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Common questions about Umami Cloud MCP in Mastra AI

Mastra AI supports automatic retries and exponential backoff. If the analytics call fails, your workflow doesn't crash; it tries again gracefully until the metrics are retrieved.

Yes. You can set up conditional branching: if `users` is below X, then execute a detailed check using `websites`. This makes the whole process very reliable.

The server touches web analytics data, specifically active user counts and granular website metrics like device type and operating system. It's all controlled via the `users` and `websites` tools.

The framework is built for failure handling. If the call to `users` times out, it automatically retries the operation until it gets a successful count.

It's designed for robustness. You get reliable web metrics—URLs, browsers, OS—and the agent framework ensures those calls complete even if there are network hiccups.

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