How to Use the Umami Cloud MCP in AutoGen

Q: What kind of analytics does Umami Cloud provide to AutoGen?

The server gives two tools: users for an immediate count of active users, and websites for rich metrics covering URLs, browser types, OS, and devices.

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Setup Umami Cloud with AutoGen

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Perplexity

Debate user activity via `users`

You set up a multi-agent conversation where one agent uses the `users` tool to get the current active count. A second agent then challenges that number based on historical context. The consensus mechanism forces agents to deliberate on whether the reported user count is unusually high or low compared to other data sources.

Negotiate site metrics with `websites`

The `websites` tool feeds detailed visitor data—OS, browsers, URLs—into an agent debate. One agent might flag a sudden spike in mobile traffic while another argues it's due to a specific browser update. The final decision emerges from the negotiation between these competing perspectives on the raw metrics.

Resolve complex analytics issues

You build a system where agents must reconcile multiple data points. For instance, an agent uses `users` for the overall count, and another calls `websites` to find out *which* specific browser type is responsible for 80% of that traffic. The final output isn't just the numbers; it's a reasoned conclusion explaining the relationship between the tools' outputs.

Setup guide

Set up Umami Cloud 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 Umami Cloud 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="Umami Cloud_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Umami Cloud 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="Umami Cloud_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

Get your connection token →

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Common questions about Umami Cloud MCP in AutoGen

AutoGen treats the MCP Server's tools as conversational inputs. Multiple agents can call `users` or `websites` independently, and then debate which tool output is most relevant to solving the overall business question.

Yes. You're not just running a script; you're building a deliberation process. The agents discuss metrics until they converge on a single, justified conclusion about the web traffic.

The server gives two tools: `users` for an immediate count of active users, and `websites` for rich metrics covering URLs, browser types, OS, and devices.

Absolutely. The tools are designed to provide distinct pieces of data (like the count vs. the composition), forcing agents to interact and build a comprehensive answer through debate.

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

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