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@enemyrr/mcp-server-pagespeed

@MCP-Mirror

代码仓库

关于 @enemyrr/mcp-server-pagespeed

Mirror of

#mcp-server#pagespeed#performance-analysis

基本信息

分类

其他

传输方式

stdio

发布者

MCP-Mirror

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "enemyrr_mcp-server-pagespeed": {
      "command": "npx",
      "args": [
        "mcp-server-pagespeed"
      ]
    }
  }
}

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is @enemyrr/mcp-server-pagespeed?

An MCP server that integrates Google PageSpeed Insights to let AI models analyze webpage performance and receive optimization suggestions through a standardized interface.

How to use @enemyrr/mcp-server-pagespeed?

Install by cloning the repository, running npm install and npm run build, then configure in Cursor IDE with the command node /absolute/path/to/build/index.js. Alternatively, run npx mcp-server-pagespeed directly from the command line. Use the analyze_pagespeed tool with a URL argument to obtain performance data.

Key features of @enemyrr/mcp-server-pagespeed

  • Real-time webpage performance analysis
  • Detailed loading experience metrics (FCP, FID)
  • Top 5 prioritized improvement suggestions
  • Comprehensive error handling for malformed URLs and API failures
  • Written in TypeScript with full type support

Use cases of @enemyrr/mcp-server-pagespeed

  • Automatically audit web page performance during development workflows
  • Generate actionable optimization recommendations from AI assistants
  • Integrate performance scoring into CI/CD or content review pipelines
  • Provide users with instant page speed insights via chat interfaces

FAQ from @enemyrr/mcp-server-pagespeed

How do I install and configure @enemyrr/mcp-server-pagespeed?

Clone the repository, run npm install and npm run build, then add it as an MCP server in Cursor IDE using the command node /absolute/path/to/build/index.js, or run npx mcp-server-pagespeed directly.

What tools does the server provide?

It provides one tool: analyze_pagespeed. It accepts a url argument and returns performance scores, loading metrics, and improvement suggestions.

What data does the analysis return?

The analysis returns an overall performance score (0–100), First Contentful Paint, First Input Delay, and the top five improvement suggestions, each with a title, description, potential impact, and current value.

What error handling is available?

The server handles invalid URLs, API request failures, connection issues, and invalid tool calls, returning detailed error messages for each case.

评论

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