How to Use the Umami Cloud MCP in CrewAI

Q: If I need to check website metrics, how does Umami Cloud work with CrewAI?

You assign an agent to use the websites tool. This agent gathers specific data points on device and browser usage for the entire site autonomously.

Run autonomous analytics operations with CrewAI and Umami Cloud MCP Server.

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Connect Umami Cloud MCP to CrewAI

Create your Vinkius account to connect Umami Cloud to CrewAI 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 CrewAI

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Autonomous Metric Analysis with MCP Server

You define a crew, giving one agent the job of checking `users` for active user totals. Another specialized agent can then use that count to trigger detailed checks via `websites`. It’s automated analysis.

Specialized Data Gathering with CrewAI

A monitoring agent can watch the session while a dedicated analytics agent uses the `websites` tool. This allows for continuous, deep reporting on things like OS or browser usage across multiple URLs.

Collaborative Web Analytics with CrewAI

The crew structure lets you delegate tasks. One agent gets the raw data from `users`, and another takes that data to format a report using the site's specific metrics. It’s shared memory in action.

Setup guide

Set up Umami Cloud MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Umami Cloud tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Umami Cloud Analyst",
    goal="Access and analyze Umami Cloud data via MCP.",
    backstory="Expert analyst with direct Umami Cloud access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Umami Cloud transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Umami Cloud Analyst",
        goal="Access and analyze Umami Cloud data via MCP.",
        backstory="Expert analyst with direct Umami Cloud access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Umami Cloud transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Umami Cloud. 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 Umami Cloud MCP in CrewAI

You pass the MCP Server URL directly to your agents, letting them access web analytics tools. The crew then executes tasks like checking user counts or site metrics without human input.

The server provides privacy-focused web metrics, including active user totals and granular details on URLs, browsers, OS, and device types. This is exposed via the `users` and `websites` tools.

Absolutely. You can set up a workflow where one agent gets the count from `users`, and another writes the final report using that metric, making it highly specialized.

You assign an agent to use the `websites` tool. This agent gathers specific data points on device and browser usage for the entire site autonomously.

It touches web analytics data, specifically active user counts and detailed website metrics. The focus remains on providing necessary operational data while keeping it private.

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