PageSpeed MCP Server: Enhancing AI Assistant Capabilities through Web Performance Analysis

Overview: What is PageSpeed MCP Server?

The PageSpeed MCP Server is an advanced integration solution that enhances Model Context Protocol (MCP) clients, such as Claude Desktop and Continue, by providing comprehensive web performance analysis capabilities. This server extends the functionality of AI applications by acting as a bridge between these models and Google's PageSpeed Insights API. By doing so, it allows for detailed performance metrics and best practices assessment to be gathered from any given URL.

🔧 Core Features & MCP Capabilities

The PageSpeed MCP Server offers a wide range of features designed to optimize web performance and provide valuable insights through the Model Context Protocol. Key capabilities include:

• Performance Metrics Analysis: Detailed analysis of site speed, including metrics such as First Contentful Paint (FCP), Largest Contentful Paint (LCP), Time to Interactive (TTI), Total Blocking Time (TBT), Cumulative Layout Shift (CLS), Speed Index, and Time to First Byte (TTFB).
• Best Practices Assessment: Evaluation of various best practices for web performance, such as HTTPS usage, JavaScript error monitoring, browser console warnings, deprecated API usage, image aspect ratio analysis, and link security checks.
• SEO Analysis: Comprehensive SEO audits, encompassing meta description validation, robots.txt validation, structured data verification, crawlable links checking, meta tags assessment, and mobile friendliness tests.
• Accessibility Audits: A thorough review of accessibility features, including ARIA attribute validation, color contrast checking, heading hierarchy analysis, alt text verification, focus management assessment, and keyboard navigation testing.
• Resource Optimization: Suggestions for optimizing resources, such as images, JavaScript bundling, CSS optimization, cache policy validation, resource minification checks, and render-blocking resource identification.

⚙️ MCP Architecture & Protocol Implementation

The PageSpeed MCP Server is designed to seamlessly integrate with MCP clients through a standardized protocol. This architecture enables the server to communicate effectively with AI applications, ensuring that performance data can be efficiently gathered and processed. The following diagram illustrates the flow of the Model Context Protocol:

graph LR;
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Data Source/Tool]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

Integration with MCP Clients

The PageSpeed MCP Server is compatible with several popular AI applications:

• Claude Desktop: Full integration, supporting all capabilities.
• Continue: Supports all features except for prompts and resources.
• Cursor: Limited to data source/tool integrations.

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Partial Support
Cursor	❌	✅	❌	Tool Integration Only

🚀 Getting Started with Installation

Installing via Smithery

To install the PageSpeed MCP Server for use with Claude Desktop, you can leverage Smithery:

npx -y @smithery/cli install mcp-pagespeed-server --client claude

Manual Installation

For those preferring a manual installation process:

npm install pagespeed-mcp-server

💡 Key Use Cases in AI Workflows

Use Case 1: Web Performance Optimization

Suppose an AI assistant is tasked with optimizing the performance of a client's e-commerce website. By integrating the PageSpeed MCP Server, the AI can analyze critical web performance metrics such as LCP and TTFB, providing actionable insights to improve user experience.

const PageSpeedMCP = require('pagespeed-mcp-server');
const mcp = new PageSpeedMCP();

await mcp.analyze('https://example.com', {
    strategy: 'mobile',
    categories: ['performance', 'accessibility'],
    locale: 'en-US'
});

Use Case 2: SEO and Accessibility Audits

In another scenario, the AI assistant needs to conduct a thorough SEO audit for a new website. By leveraging the PageSpeed MCP Server, it can perform comprehensive analysis of meta tags, robots.txt validation, and mobile friendliness tests.

const results = await mcp.analyze('https://example.com', {
    strategy: 'desktop',
    categories: ['seo'],
    locale: 'en-US'
});

🔌 Integration with MCP Clients

To integrate the PageSpeed MCP Server into an AI application, you need to add it to your configuration file. For instance, for Claude Desktop integration:

{
  "pagespeed": {
    "command": "node",
    "args": ["path/to/mcp-pagespeed-server/dist/index.js"]
  }
}

📊 Performance & Compatibility Matrix

The performance and compatibility matrix highlights the server's effectiveness across different platforms and environments:

• Windows (x64, x86)
• Linux (x64)
• macOS (x64, arm64)

Advanced Configuration & Security

Custom Configuration Options

You can further customize PageSpeed MCP Server behavior by providing additional parameters during the analysis request.

{
    "strategy": "mobile", // or "desktop"
    "category": ["performance", "accessibility", "best-practices", "seo"],
    "locale": "en",
    "threshold": {
        "performance": 90,
        "accessibility": 100,
        "best-practices": 90,
        "seo": 90
    }
}

Error Handling

The MCP server includes robust error handling mechanisms to ensure smooth operation:

• Invalid URLs
• Network timeouts
• API rate limiting
• Invalid parameters
• Server-side errors

FAQs (Frequently Asked Questions)

1. What are the minimum system requirements for using the PageSpeed MCP Server?

   • The server is designed to be compatible with various platforms, including Windows, Linux, and macOS.

2. How does the PageSpeed MCP Server handle network timeouts?

   • If a timeout occurs during analysis, it logs an error and retries based on configurable limits.

3. Can I use multiple MCP servers in my AI application setup?

   • Yes, you can integrate multiple MCP servers to cover different data sources or tools, enhancing the overall functionality.

4. Are there any known compatibility issues with specific AI clients?

   • There are no known major issues, but some features may not be fully compatible with all clients. Refer to the client compatibility matrix for details.

5. How is user privacy protected during analysis?

   • User data is anonymized and processed only within the context of performance and SEO analyses, ensuring compliance with privacy regulations.

👨‍💻 Development & Contribution Guidelines

Building from Source

To contribute to or customize the PageSpeed MCP Server:

1. Clone the repository:

   git clone https://github.com/phialsbasement/mcp-pagespeed-server
   

2. Navigate into the directory and install dependencies:

   cd mcp-pagespeed-server
   npm install
   npm run build
   

3. Run the tests to ensure everything is working correctly:

   npm run test
   

Forking Repository

1. Fork the repository on GitHub.
2. Create a feature branch and commit your changes.
3. Push the branch to your forked GitHub account.
4. Open a Pull Request to contribute back.

🌐 MCP Ecosystem & Resources

For developers looking to integrate MCP into their AI applications, several resources are available:

• MCP Server Documentation: Detailed guides on server setup and integration.
• Smithery CLI Tool: For easy integration of MCP servers with various clients.
• Community Forums: A place for developers to share ideas and solve problems collectively.

License

The PageSpeed MCP Server is licensed under the MIT License. Please refer to the LICENSE file in the repository for full details.

---

This comprehensive documentation outlines the capabilities, installation, use cases, and integration of the PageSpeed MCP Server, positioning it as a powerful tool for enhancing AI applications through web performance analysis.