What are the prerequisites to run the server?
You need to install `uv` and have Umami account credentials: API URL, username, password, and team ID.
@MCP-Mirror
基本情報
Mirror of
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"analytics_service": {
"command": "uv",
"args": [
"--directory",
"/path/to/analytics_service",
"run",
"analytics-service"
],
"env": {
"UMAMI_API_URL": "https://example.com",
"UMAMI_USERNAME": "yourUmamiUsername",
"UMAMI_PASSWORD": "yourUmamiPassword",
"UMAMI_TEAM_ID": "yourUmamiTeamId"
}
}
}
}Retrieve a list of websites and their IDs in your Umami account
Get key metrics like pageviews, visitors, bounce rate for a website
Analyze specific metrics like URLs, referrers, browsers, countries
Get time-series pageview data with customizable intervals
Monitor current number of active visitors on a website
Retrieve session IDs for specific events or time periods
Get detailed activity data for a specific session ID
Perform semantic search on many user journeys, returning the most relevant chunks for a given question
Capture visual snapshots of webpages
Retrieve and analyze webpage HTML source code
A Model Context Protocol (MCP) server that enhances Claude with access to website analytics data from Umami. It enables Claude to analyze user behavior, track website performance, and generate data-driven insights.
Install uv, then add the MCP server configuration to your Claude Desktop config file (claude_desktop_config.json) with the Umami API URL, username, password, and team ID. When you open Claude Desktop, the server connects automatically. Use the built-in "Create Dashboard Prompt" or interact via natural language; Claude will choose the appropriate tools.
You need to install `uv` and have Umami account credentials: API URL, username, password, and team ID.
Add a JSON entry under `mcpServers` in your `claude_desktop_config.json`, specifying `uv` as the command and the required environment variables (`UMAMI_API_URL`, `UMAMI_USERNAME`, `UMAMI_PASSWORD`, `UMAMI_TEAM_ID`).
Yes, define a prompt structure in `list_prompts()` and implement its logic in `get_prompt()` within `src/analytics_service/server.py`.
The `get_docs` tool chunks journey data, embeds them with a sentence transformer, and returns only the most relevant segments via semantic search, preventing context window overload.

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