How to Use the Matomo Alternative MCP in AutoGen

Q: How do I add Matomo Alternative tools to my AutoGen agents?

Install the extension with pip install -U "autogen-extmcp". Use the mcpservertools() function with your server URL, and then pass the resulting tool list directly to your AssistantAgent constructor.

Q: What's the best way to visualize data from Matomo Alternative in an AutoGen conversation?

Have one agent call getimagegraph. It will receive PNG data, which it can save to a file. The agent can then print the file path in the chat, like "chart.png saved", so a human or another tool can see it.

Let your AutoGen agents debate your analytics. Give them the tools to pull, analyze, and challenge website performance data.

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Connect Matomo Alternative MCP to AutoGen

Create your Vinkius account to connect Matomo Alternative to AutoGen 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 Alternative with AutoGen

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Fuel Agent Debates with Data

Give your specialized agents the facts they need to argue their case. Your "MarketingAgent" can use `get_visits_summary` to show a spike in traffic, claiming a campaign is a success. It's simple, direct evidence for its position. Then, a "FinanceAgent" can counter by calling `get_processed_report` to pull deeper conversion metrics and revenue data. This agent might argue that while traffic is up, the bounce rate is high and revenue is flat, challenging the initial conclusion. This is how AutoGen works best—with competing agents using real data.

Collaborative Event Tracking

Your team of agents can work together to log user activity. A "UI_Agent" could detect a button click and propose a `track_action` call. Before it executes, a "DataQualityAgent" could inspect the proposed event and either approve it or suggest batching it with `track_bulk`. This conversational approach to execution ensures you're tracking data correctly. It prevents a single rogue agent from flooding your analytics with bad data, because another agent acts as a check and balance. This MCP server provides the necessary tools for them to use.

An MCP Server for Multi-Agent Systems

Your agents can plan their work by first understanding what's possible. One agent can be tasked with calling `get_report_metadata` to get a list of all available reports. It then shares this list with the group, allowing them to strategize. Based on that list, a "PlannerAgent" can assign tasks. It might tell the "ReportingAgent" to use `get_image_graph` for a presentation and the "AnalystAgent" to use `get_wp_processed_report` for a deep dive. The agents decide how to use the tools this MCP server exposes.

Setup guide

Set up Matomo Alternative MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Matomo Alternative tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

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

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Matomo Alternative_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Matomo Alternative data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

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

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Matomo Alternative_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Matomo Alternative data")
    print(result.messages[-1].content)

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 Alternative MCP in AutoGen

Install the extension with `pip install -U "autogen-ext[mcp]"`. Use the `mcp_server_tools()` function with your server URL, and then pass the resulting tool list directly to your `AssistantAgent` constructor.

Yes, that's the point. One agent can be pulling a report with `get_processed_report` while another is tracking an event with `track_action`. The Vinkius MCP platform handles the concurrent requests.

You can design a conversation where one agent proposes an action and another critiques it. For example, an agent could propose `track_action`, and a "PerformanceAgent" could intervene and say, "We have 10 events queued, let's use `track_bulk` instead."

Have one agent call `get_image_graph`. It will receive PNG data, which it can save to a file. The agent can then print the file path in the chat, like "[chart.png saved]", so a human or another tool can see it.

Absolutely. Vinkius isolates every tool call. The server handling the requests for your `page view` or `bulk tracking` data is ephemeral and stateless. It knows nothing about previous or future requests.

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