lighthouse-mcp
Related Servers
Alternatives to lighthouse-mcp
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityDmaintenanceEnables AI models to perform Google Lighthouse website performance analysis, including Core Web Vitals, accessibility, SEO audits, and actionable optimization recommendations. Provides comprehensive web performance insights through natural language interactions.4,086 npm4MIT
- AlicenseAqualityDmaintenanceCore Web Vitals analysis powered by Lighthouse. Four tools: analyze a URL, compare two URLs, check against thresholds, or crawl an entire site. Works with Claude Code, Cursor, Windsurf, and any MCP-compatible AI tool.410 npmMIT
- AlicenseAqualityCmaintenanceConnects LLMs to Google PageSpeed Insights to analyze web performance, accessibility, SEO, and best practices, enabling AI assistants to audit and improve any web page.10MIT
- AlicenseAqualityCmaintenanceEnables AI coding agents to retrieve real-user performance metrics and 28-day field trends, run Lighthouse lab audits, and access remediation playbooks for LCP, INP, and CLS.7249 PyPIMIT
- AlicenseNot gradedqualityDmaintenanceEnables AI assistants to control and inspect a live Chrome browser for automated web debugging, performance analysis, and Lighthouse audits. It allows agents to capture screenshots, monitor network requests, and measure Core Web Vitals using plain-English prompts.2,848,483 npmApache 2.0
- AlicenseNot gradedqualityCmaintenanceEnables AI assistants and coding agents to audit, compare, and optimize websites for performance, SEO, AEO, GEO, and AI crawler accessibility directly from chat or development workflows.MIT
TDQS
Scored across 2 tools
Both tools relate to Lighthouse audits, but they have distinct purposes: run_audit executes a full audit, while get_performance_score focuses on retrieving only the performance metric. However, get_performance_score is essentially a subset of run_audit, so an agent might be unsure when to use one over the other without additional context.
Both tool names follow a clear verb_noun pattern (run_audit, get_performance_score) with consistent snake_case and action-first style. The naming is predictable and unambiguous in structure.
With only 2 tools, the server feels under-scoped for a Lighthouse MCP. A typical Lighthouse workflow would expect a richer set of operations, such as fetching full report details, comparing scores, or handling multiple categories. The minimal count limits utility and feels thin.
The tool surface is severely incomplete for a Lighthouse server. It only covers running an audit and extracting the performance score, omitting other standard audit categories (accessibility, SEO, best practices) and report retrieval. Agents would hit dead ends if they need any other Lighthouse data.