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Lighthouse

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priyankark

Allows AI assistants such as Amp/Cline/Cursor/Claude Code/Codex/GitHub Copilot to use Google's lighthouse tool to measure perf metrics for your webpage. You can then run an agentic loop and get the assistants to optimize those metrics!

Publisherpriyankark
Repositorylighthouse-mcp
LanguageJavaScript
Forks
15
Stars
140
Available tools
2
Transport typestdio
Categories
LicenseMIT
Links
  • Connect tools to AI workflows

    Lighthouse exposes MCP capabilities that can be used by compatible AI clients and agents.

  • 2 available tools

    Browse the callable actions below, including names and descriptions when provided by the server.

  • Ready-to-copy setup

    Use the installation snippets to configure this server in your preferred MCP client.

  • Open source signals

    140 stars and 15 forks from the linked repository.

Lighthouse MCP Server

An MCP server that wraps around Google's Lighthouse tool to help measure various performance metrics for web pages.

Features

  • Run comprehensive Lighthouse audits on any URL
  • Get performance scores and metrics
  • Configure device emulation (mobile/desktop)
  • Control network throttling
  • Select specific audit categories

Installation

Option 1: From MCP Registry (Recommended)

This server is available in the Model Context Protocol Registry. Install it using your MCP client or Claude Desktop.

Option 2: Using npx

You can run the tool directly using npx without installation:

bash
npx lighthouse-mcp

Option 3: Global Installation

Install the package globally from npm:

bash
npm install -g lighthouse-mcp

Then run it:

bash
lighthouse-mcp

Option 4: Local Development

  1. Clone this repository
  2. Install dependencies:
    bash
    npm install
  3. Build the project:
    bash
    npm run build
  4. Run the server:
    bash
    npm start

MCP Configuration

When installed via npm (global or npx)

Add the following to your MCP settings configuration file:

json
{
  "mcpServers": {
    "lighthouse": {
      "command": "npx",
      "args": ["lighthouse-mcp"],
      "disabled": false,
      "autoApprove": []
    }
  }
}

When using local development version

Add the following to your MCP settings configuration file:

json
{
  "mcpServers": {
    "lighthouse": {
      "command": "node",
      "args": ["/absolute/path/to/lighthouse-mcp/build/index.js"],
      "disabled": false,
      "autoApprove": []
    }
  }
}

Replace /absolute/path/to/lighthouse-mcp with the actual path to this project.

Available Tools

run_audit

Run a comprehensive Lighthouse audit on a URL.

Parameters:

  • url (required): The URL to audit
  • categories (optional): Array of categories to audit (defaults to all)
    • Options: "performance", "accessibility", "best-practices", "seo", "pwa"
  • device (optional): Device to emulate (defaults to "mobile")
    • Options: "mobile", "desktop"
  • throttling (optional): Whether to apply network throttling (defaults to true)

Example:

json
{
  "url": "https://example.com",
  "categories": ["performance", "accessibility"],
  "device": "desktop",
  "throttling": false
}

get_performance_score

Get just the performance score for a URL.

Parameters:

  • url (required): The URL to audit
  • device (optional): Device to emulate (defaults to "mobile")
    • Options: "mobile", "desktop"

Example:

json
{
  "url": "https://example.com",
  "device": "mobile"
}

Example Usage

Once the MCP server is configured, you can use it with Claude:

What's the performance score for example.com?

Claude will use the get_performance_score tool to analyze the website and return the results.

Requirements

  • Node.js 16+
  • Chrome/Chromium browser (for Lighthouse)

Endorsements

Installation

TypingMind
Prerequisites:

Node.js 18+

{
  "mcpServers": {
    "lighthouse": {
      "command": "npx",
      "args": [
        "lighthouse-mcp"
      ]
    }
  }
}

Available Tools

  • run_audit

    Run a Lighthouse audit on a URL

  • get_performance_score

    Get just the performance score for a URL

Use Lighthouse MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once Lighthouse is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

1

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

  1. Open TypingMind in your browser.
  2. Click the Settings icon.
  3. Go to Advanced Settings.
  4. Open the Model Context Protocol section.
  5. Click Setup Connector and choose This Device.
TypingMind MCP connector setup screen with This Device selected
2

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

  1. Copy the setup command shown by TypingMind.
  2. Open Terminal on macOS or Windows Terminal on Windows.
  3. Paste and run the command.
  4. Approve the package install if Terminal asks you to proceed.
  5. Keep the Terminal window running while using MCP tools.
3

Add Lighthouse as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

  1. Wait until the connector status shows Ready.
  2. Click Edit Servers.
  3. Paste the Lighthouse MCP server configuration.
  4. Save the server list.
  5. Refresh if you want to confirm the connector is still ready.
TypingMind MCP settings showing active server and Edit Servers button
{
  "mcpServers": {
    "lighthouse": {
      "command": "npx",
      "args": [
        "-y",
        "lighthouse-mcp"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the Lighthouse MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

  1. Open the Plugins page in TypingMind.
  2. Enable the Lighthouse MCP tools.
  3. Start a chat and choose the AI model you want to use.
  4. Use the MCP tools in chat or assign them to an AI agent.
  5. Switch to another AI model whenever needed without reconnecting MCP.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use Lighthouse to help me with this task?
Lighthouse
Sure. I read it.
Here is what I found using Lighthouse.

Frequently asked questions

What is the Lighthouse MCP server used for?

Lighthouse is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use Lighthouse MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use Lighthouse with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use Lighthouse MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With Lighthouse connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect Lighthouse MCP to TypingMind?

Lighthouse runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does Lighthouse MCP provide in TypingMind?

Lighthouse exposes 2 MCP tools that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use Lighthouse MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If Lighthouse requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

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