How to Use the Umami (Privacy Analytics) MCP in AutoGen

Q: What exact data type does Umami (Privacy Analytics) touch? (AutoGen)

The server handles event details, allowing your agents to analyze the specific sequence and context of user actions on the site.

Achieve consensus-driven decisions on analytics with Umami (Privacy Analytics) and AutoGen.

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Connect Umami (Privacy Analytics) MCP to AutoGen

Create your Vinkius account to connect Umami (Privacy Analytics) 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 Umami (Privacy Analytics) with AutoGen

Ask AI about this MCP

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Claude

Perplexity

Debating the best reporting scope.

You can set up a multi-agent conversation where one agent checks for immediate performance issues using `get_realtime_stats`. A second agent challenges that data, demanding broader context by running `create_funnel_report`. The third agent then mediates, calling `create_report` with the final agreed scope.

Negotiating user access and roles.

Run a debate between security and operations agents. One might flag that a user needs elevated permissions; another might argue for least privilege. They negotiate by calling `get_user` first, then deciding whether to proceed with an update using `update_user`.

Determining site status through consensus.

Want to know if a website is still active? One agent checks the basic existence via `get_website`. Another runs `get_website_active` for current user count. They must agree on the criteria, and only then does the system proceed with necessary actions like running `reset_website`.

Setup guide

Set up Umami (Privacy Analytics) 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 (Privacy Analytics) 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 (Privacy Analytics)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

Get your connection token →

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 (Privacy Analytics)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Umami (Privacy Analytics) 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 Umami. 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 (Privacy Analytics) MCP in AutoGen

AutoGen allows multiple agents to challenge each other's tool calls. You can set up a scenario where one agent suggests `create_retention_report`, and another challenges it by checking historical metrics first.

Yes. You can make agents debate whether the current website statistics (`get_website_stats`) are sufficient or if a deeper dive into specific events is required.

The system provides full control. Agents can debate whether to add, remove, or modify teams using tools like `create_team` and `delete_user`, ensuring all changes are vetted.

Agents can validate the status of resources. For instance, they check if a website is still managed using `get_website` before any other action is taken.

The server handles `event details`, allowing your agents to analyze the specific sequence and context of user actions on the site.

Use it with your favorite AI tools

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