Umami Analytics MCP Server: Enhancing Claude's Capabilities with Website Analytics Data

Overview: What is Umami Analytics MCP Server?

The Umami Analytics MCP Server is a specialized Model Context Protocol (MCP) server that integrates Claude Desktop, Continue, Cursor, and other AI applications with the robust data analytics capabilities of Umami. This server enables these AI applications to analyze user behavior, track website performance metrics, and generate actionable insights directly within their workflows.

🔧 Core Features & MCP Capabilities

The Umami Analytics MCP Server provides a suite of tools that allow Claude Desktop to interact seamlessly with Umami API endpoints. These tools enable detailed analysis of user journeys, real-time visitor activity tracking, webpage content examination, historical analytics data retrieval, and more. By leveraging the capabilities of Umami's powerful analytics platform through MCP, this server significantly enhances Claude's analytical prowess.

Available Tools Overview

The following are the key functionalities provided by the Umami Analytics MCP Server:

• get_websites: Retrieve a list of websites and their IDs from your Umami account.
• get_website_stats: Fetch key metrics such as pageviews, visitors, and bounce rate for a specific website.
• get_website_metrics: Analyze detailed metrics like URLs, referrers, browsers, countries, etc., for a given website.
• get_pageview_series: Obtain time-series data on pageviews with customizable intervals.
• get_active_visitors: Monitor the current number of active visitors on your website.
• get_session_ids: Retrieve session IDs related to specific events or time periods.
• get_tracking_data: Fetch detailed activity records from a particular session ID.
• get_docs: Conduct semantic search on user journeys to return relevant segments for analysis.
• get_screenshot: Capture visual snapshots of webpages using the Crawl4AI web crawler.
• get_html: Retrieve and analyze HTML source code from webpages.

Each tool comes with a clear description and parameters that can be passed to provide context and ensure accurate selection by Claude Desktop during analysis tasks. These tools operate primarily through data retrieval from Umami, but the get_docs tool integrates additional semantic search functionality to address context window limitations in Claude Desktop.

⚙️ MCP Architecture & Protocol Implementation

The Umami Analytics MCP Server follows a standard Model Context Protocol (MCP) architecture that facilitates seamless integration between diverse AI applications and specific tools or data sources like Umami. This protocol ensures interoperability, enabling the server to adapt to various MCP clients while maintaining consistent behavior across different environments.

Configuration Example:

{
  "mcpServers": {
    "umami-analytics": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-umami"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

This configuration demonstrates how to set up the server with a specific environment variable, API_KEY, which is crucial for authenticating requests and accessing Umami's API endpoints. Proper MCP client compatibility ensures that these settings are recognized correctly by various AI applications.

🚀 Getting Started with Installation

To begin using the Umami Analytics MCP Server, follow these steps:

1. Ensure you have Node.js installed on your system.
2. Navigate to a preferred directory in your terminal and clone the repository from GitHub or download it as a ZIP file.
3. Install dependencies by running npm install within the project directory.
4. Set up environment variables as required, such as the Umami API key.
5. Launch the server using npx <command> with the specified arguments.

Installation Command:

npx -y @modelcontextprotocol/server-umami --env.API_KEY=your-api-key

💡 Key Use Cases in AI Workflows

The Umami Analytics MCP Server is designed to address critical needs in AI workflows, particularly those involving web analytics and data-driven decision-making. Here are two realistic use cases highlighting the server's capabilities:

Use Case 1: Real-Time Performance Monitoring

AI applications can utilize real-time visitor tracking data provided by the get_active_visitors tool. By integrating this into monitoring dashboards or alert systems, developers can quickly identify spikes in traffic or anomalies that require immediate attention.

Use Case 2: Detailed User Behavior Analysis

The get_docs and semantic search capabilities within the server enable deep analysis of user interactions with a website. This information is invaluable for optimizing content strategies, improving UX design, and personalizing marketing efforts based on real-world data.

🔌 Integration with MCP Clients

The Umami Analytics MCP Server maintains compatibility across multiple MCP clients, ensuring broad usability and flexibility:

• Claude Desktop: Full Support
  • Provides all the necessary tools and features for comprehensive web analytics integration.
• Continue: Full Support
  • Leverages the server to access detailed user behavior data efficiently.
• Cursor: Tools Only
  • While Cursor does not support prompts fully, it can still benefit from the suite of analysis tools.

📊 Performance & Compatibility Matrix

Below is a compatibility matrix showcasing how the Umami Analytics MCP Server integrates with different MCP clients:

MCP Client	Resources	Tools	Prompts
Claude Desktop	✅	✅	✅
Continue	✅	✅	✅
Cursor	❌	✅	❌

This matrix highlights the comprehensive support offered to users of various MCP clients, ensuring a smooth integration process and enhanced functionality for web analytics within AI workflows.

🛠️ Advanced Configuration & Security

To ensure optimal performance and security when using the Umami Analytics MCP Server, consider the following advanced configuration options:

• Environment Variables: Securely store sensitive information like API keys.
• Authentication Tokens: Implement token-based authentication to protect API access.
• Firewall Rules: Configure firewall rules to restrict unauthorized access to critical APIs.

Security best practices include regularly updating dependencies and monitoring logs for suspicious activities. Following these guidelines helps safeguard your data and maintain operational integrity.

❓ Frequently Asked Questions (FAQ)

Q1: Can the Umami Analytics MCP Server be used with other AI applications besides Claude Desktop?

Yes, while primarily compatible with Claude Desktop, this server can integrate with Continue and Cursor, although certain functionalities like prompts may not be fully supported in all cases.

Q2: How does the get_screenshot tool work for capturing visual data?

The get_screenshot tool uses Crawl4AI to capture high-resolution images of webpages. This process is efficient and scalable, making it ideal for maintaining consistent visual records across a wide range of sites.

Q3: Is there a limit on how many requests can be made to Umami's API?

Yes,Umami has rate limiting in place to protect against abuse. Check their official documentation for specific limits and strategies to handle high-volume data retrieval operations responsibly.

Q4: Can I customize the appearance of AI applications using data from this server?

While the Umami Analytics MCP Server provides raw data, you can integrate it with front-end frameworks or styling tools in AI applications to visually represent analytics. Customization possibilities depend on the specific application and its design capabilities.

Q5: Are there any known issues related to cross-origin requests (CORS) when accessing Umami's API?

Cross-origin requests might require specific CORS settings within Umami, especially if the client is hosted at a different domain. Consult Umami’s documentation for guidance on configuring CORS headers appropriately.

👨‍💻 Development & Contribution Guidelines

For developers interested in contributing to or extending the capabilities of the Umami Analytics MCP Server, the following guidelines are provided:

• Issue Reporting: Report bugs and feature requests via the project's issue tracker.
• Pull Requests: Submit pull requests for code enhancements and new features. Ensure documentation is updated accordingly.
• Code Standards: Adhere to established coding standards and best practices.

Contributions should focus on enhancing both functionality and usability, ensuring that future updates align with broader MCP community goals and standards.

🌐 MCP Ecosystem & Resources

The Umami Analytics MCP Server integrates into a larger MCP ecosystem, allowing it to benefit from collaborative efforts and shared resources within the AI development community. Explore related projects and forums for additional insights and support as you develop your applications.

For more detailed information and community collaboration, visit the official MCP GitHub repositories and participate in relevant discussions.

Conclusion

The Umami Analytics MCP Server stands as a pivotal bridge between web analytics tools like Umami and cutting-edge AI applications such as Claude Desktop. By adhering to the Model Context Protocol, this server ensures robust integration, enabling powerful features like real-time visitor tracking and detailed user behavior analysis that can significantly enhance the capabilities of any AI-driven workflow. Whether you're developing enterprise solutions or crafting personalized web experiences, the Umami Analytics MCP Server offers unparalleled functionality for seamless data analytics integration.