How to Use the Matomo MCP in CrewAI

Q: How do I configure Matomo for my CrewAI agents?

Add the Vinkius URL directly to the mcps list in your agent definition. The framework handles the HTTP transport and tool registration automatically.

Q: Can I restrict which Matomo tools specific CrewAI agents access?

Yes. Use the MCPServerHTTP class from crewai.mcp and apply a toolfilter. You can give the researcher agent access to listsites while restricting the analyst.

Q: How does the crew track multiple websites?

The setup starts with listsites to map the environment. The agents then iterate through the site IDs, running parallel checks on each property.

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Connect Matomo MCP to CrewAI

Create your Vinkius account to connect Matomo 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 Matomo with CrewAI

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Perplexity

Delegated Goal Analysis

`get_goals` and `get_visits_summary` supply the raw data your CrewAI agents need to evaluate campaign performance. A researcher agent pulls the numbers, and an analyst agent interprets the conversion rates. This division of labor prevents context limits from breaking the run. You isolate the data extraction to one agent while the rest of the crew handles the strategic reporting.

Autonomous SEO Auditing

`get_top_referrers`, `get_top_websites`, and `get_top_socials` allow your SEO agents to autonomously audit inbound traffic. They map out exactly which channels drive the most volume without waiting for a human to export a CSV. You pass the Vinkius endpoint directly into the agent's `mcps` array. The crew immediately understands how to query the traffic sources and cross-reference them against historical baselines.

Real-Time Matomo MCP Server Monitoring

`get_live_last_visits` and `get_visitor_profile` turn a passive reporting setup into an active monitoring system. Your moderator agent watches the live session feed and flags anomalous behavior as it occurs. Combine this with `get_top_pages` to track sudden traffic spikes. When a specific URL starts trending, the crew detects the surge and triggers an automated alert sequence.

Setup guide

Set up Matomo 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 Matomo tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Matomo 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="Matomo Analyst",
        goal="Access and analyze Matomo data via MCP.",
        backstory="Expert analyst with direct Matomo access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Matomo 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 Matomo. 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 Matomo MCP in CrewAI

Add the Vinkius URL directly to the `mcps` list in your agent definition. The framework handles the HTTP transport and tool registration automatically.

Yes. Use the `MCPServerHTTP` class from `crewai.mcp` and apply a `tool_filter`. You can give the researcher agent access to `list_sites` while restricting the analyst.

They do. CrewAI uses shared memory. If one agent pulls data via `get_site_details`, the next agent in the sequence reads that context without making a redundant API call.

The setup starts with `list_sites` to map the environment. The agents then iterate through the site IDs, running parallel checks on each property.

The MCP server exposes hard conversion numbers and goal completions through `get_goals`. Vinkius completely isolates the execution environment, tearing down the container the moment the agent finishes its task to guarantee zero data leakage.

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