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Umami Analytics MCP Server

@jakeyShakey

代码仓库

关于 Umami Analytics MCP Server

暂无概览

基本信息

分类

数据与分析

许可证

MIT

运行时

python

传输方式

stdio

发布者

jakeyShakey

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "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"
      }
    }
  }
}

工具

10

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

概览

What is Umami Analytics MCP Server?

An MCP server that gives Claude access to Umami website analytics data, enabling analysis of user behavior, website performance, and data-driven insights. It connects to your Umami platform (self-hosted or Umami Cloud) and provides tools for retrieving metrics, tracking visitors, capturing screenshots, and analyzing page content.

How to use Umami Analytics MCP Server?

Configure Claude Desktop with the analytics_service MCP server entry pointing to your local copy of the server, supplying UMAMI_API_URL, UMAMI_USERNAME, UMAMI_PASSWORD, and UMAMI_TEAM_ID. After starting Claude Desktop, 10 tools become available. Use the built-in “Create Dashboard Prompt” or natural language requests to have Claude analyze your data.

Key features of Umami Analytics MCP Server

  • Retrieve website list and IDs from your Umami account
  • Get key metrics: pageviews, visitors, bounce rate
  • Analyze metrics by URL, referrer, browser, country
  • View time-series pageview data with customizable intervals
  • Monitor real‑time active visitors on a website
  • Retrieve session IDs and detailed tracking data
  • Semantic search over user journeys (embedding + chunking)
  • Capture webpage screenshots and HTML source code via Crawl4AI

Use cases of Umami Analytics MCP Server

  • Ask Claude to build a visual dashboard of your website analytics
  • Analyze user journeys to identify pain points and drop‑off areas
  • Monitor current visitor activity and compare with historical trends
  • Get performance recommendations enriched with live page screenshots
  • Automate recurring analytics reports using custom prompts

FAQ from Umami Analytics MCP Server

What tools does the server provide?

The server provides 10 tools: get_websites, get_website_stats, get_website_metrics, get_pageview_series, get_active_visitors, get_session_ids, get_tracking_data, get_docs, get_screenshot, and get_html.

What are the prerequisites and runtime requirements?

You need uv installed (pip install uv). The server runs locally via uv and connects to your Umami instance (self‑hosted or Umami Cloud). It also uses the open‑source Crawl4AI web crawler for screenshots and HTML retrieval.

Where does analytics data live?

All analytics data stays in your Umami instance. The server queries the Umami API and does not store any data. User journeys are temporarily embedded and searched in memory (using a Hugging Face sentence transformer) to avoid context window issues.

Are there any known limits?

Screenshots are downsampled to reduce size and avoid context window limits. The get_docs tool performs semantic search on chunked user journeys to keep token usage low.

What transports and authentication does the server use?

The server uses standard MCP transport (stdio) via the uv command. Authentication is handled by supplying Umami credentials (UMAMI_USERNAME, UMAMI_PASSWORD) and a team ID in the environment variables.

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